diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index e5783e6..64f4968 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -89,7 +89,6 @@ def dataset_generator(): masks = data[image_path]["masks"] for mask, label in zip(masks, labels): poly = [] - print(mask) poly += [ (point[0] / width, point[1] / height) for point in mask ] diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb index 2ae760e..a7a5d30 100644 --- a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -21,7 +21,7 @@ "base_uri": "https://localhost:8080/" }, "id": "b5_2ivH03etO", - "outputId": "d4c24006-c285-49b8-ad83-be674983238a" + "outputId": "c92b1e2e-cd3e-4a7d-8be6-776e0dfad5bc" }, "outputs": [ { @@ -30,12 +30,51 @@ "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.8/168.8 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m75.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.7/125.7 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m553.5/553.5 kB\u001b[0m \u001b[31m30.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m523.2/523.2 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.8/318.8 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.0/16.0 MB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m226.2/226.2 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m266.4/266.4 kB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m80.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m378.2/378.2 kB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.5/43.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m223.8/223.8 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m68.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m62.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.0/78.0 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.6/316.6 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.7/117.7 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for datadreamer (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.3 which is incompatible.\n", + "google-cloud-firestore 2.16.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.3 which is incompatible.\n", + "tensorboard 2.17.0 requires protobuf!=4.24.0,<5.0.0,>=3.19.6, but you have protobuf 5.28.3 which is incompatible.\n", + "tensorflow 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.28.3 which is incompatible.\n", + "tensorflow-metadata 1.16.1 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.28.3 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" ] } ], "source": [ - "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@feat/add-instance-segmentation" + "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@dev" ] }, { @@ -57,7 +96,7 @@ "source": [ "Make sure you are using the GPU runtime type (in Google Colab).\n", "\n", - "~4 min to generate 28 images\n", + "~4 min to generate 30 images\n", "\n", "~43 secs to annotate them" ] @@ -71,7 +110,7 @@ "base_uri": "https://localhost:8080/" }, "id": "6ab1e2f9", - "outputId": "e055777f-91db-4da6-89e8-08cc5960dedf", + "outputId": "6f57eb7a-f261-46bc-e574-3631cade8660", "scrolled": true }, "outputs": [ @@ -79,218 +118,2061 @@ "name": "stdout", "output_type": "stream", "text": [ + "2024-10-24 20:24:16.241793: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-10-24 20:24:16.272474: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-10-24 20:24:16.282212: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-10-24 20:24:16.304239: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-10-24 20:24:17.906040: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n", "\u001b[32mINFO \u001b[0m Profanity filter is checking classes: \u001b[1m[\u001b[0m\u001b[32m'tractor'\u001b[0m, \u001b[32m'horse'\u001b[0m, \u001b[32m'bear'\u001b[0m\u001b[1m]\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py\u001b\\\u001b[2mprofanity_filter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/prompt_generation/profanity_filter.py#170\u001b\\\u001b[2m170\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[32mINFO \u001b[0m Initializing SDXL Turbo on cuda\u001b[33m...\u001b[0m \u001b]8;id=234053;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py\u001b\\\u001b[2msdxl_turbo_image_generator.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///usr/local/lib/python3.10/dist-packages/datadreamer/image_generation/sdxl_turbo_image_generator.py#42\u001b\\\u001b[2m42\u001b[0m\u001b]8;;\u001b\\\n", - "Loading pipeline components...: 100% 7/7 [00:05<00:00, 1.19it/s]\n", - "Generating images: 0% 0/28 [00:00" ] @@ -424,7 +2306,7 @@ "base_uri": "https://localhost:8080/" }, "id": "a167a842", - "outputId": "4e5f7ecb-b795-4f10-828d-b8378ad5d491" + "outputId": "715988c2-ab27-4ce2-b12c-2fa01188c537" }, "outputs": [ { @@ -454,10 +2336,23 @@ "execution_count": 7, "id": "982e475e", "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, "id": "982e475e", + "outputId": "1f4cb9f5-1d01-4882-a730-434e5122546f", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/877.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m877.1/877.1 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], "source": [ "!pip install -q ultralytics" ] @@ -467,9 +2362,31 @@ "execution_count": 8, "id": "184cf0fa", "metadata": { - "id": "184cf0fa" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "184cf0fa", + "outputId": "dcc43a26-bc78-4d3d-ddb3-6932a8584df9" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating new Ultralytics Settings v0.0.6 file ✅ \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", + "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6.74M/6.74M [00:00<00:00, 110MB/s]\n" + ] + } + ], "source": [ "from ultralytics import YOLO\n", "\n", @@ -483,20 +2400,10 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "73419dc7b5a942bbbdbfe9e0a2552c23", - "0e7d0e4096284f8489b30be03692e919", - "0b7fe81c0e7943bda46c9aa63b9ce8ea", - "23b7f9ae5b094585b21bd36f7cde08ca", - "b55d573195ad489cb621b47b1e1c7d52", - "571d1e0c1e704169a5a91b03d0d589e7", - "31693faf3df247a4aface2c6706d9b64", - "f81e7e7eb9f6491786aca2d98dcd9c71" - ] + "height": 1000 }, "id": "bb4e6754", - "outputId": "eb28d115-aed1-4fe6-a595-dfdcb26a7afe", + "outputId": "66b28d5a-6544-46fa-ee73-3074f141e981", "scrolled": true }, "outputs": [ @@ -504,8 +2411,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", - "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8n-seg.pt, data=generated_dataset_yolo/data.yaml, epochs=200, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train4, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train4\n", + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8n-seg.pt, data=generated_dataset_yolo/data.yaml, epochs=200, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train\n", + "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 755k/755k [00:00<00:00, 24.2MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Overriding model.yaml nc=80 with nc=3\n", "\n", " from n params module arguments \n", @@ -535,7 +2456,7 @@ "YOLOv8n-seg summary: 261 layers, 3,264,201 parameters, 3,264,185 gradients, 12.1 GFLOPs\n", "\n", "Transferred 381/417 items from pretrained weights\n", - "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train4', view at http://localhost:6006/\n" + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train', view at http://localhost:6006/\n" ] }, { @@ -548,7 +2469,7 @@ { "data": { "text/html": [ - "Tracking run with wandb version 0.18.3" + "Tracking run with wandb version 0.18.5" ], "text/plain": [ "" @@ -574,7 +2495,21 @@ "output_type": "stream", "text": [ "Freezing layer 'model.22.dfl.conv.weight'\n", - "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5.35M/5.35M [00:00<00:00, 102MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n" ] }, @@ -582,7 +2517,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/generated_dataset_yolo/train/labels... 22 images, 0 backgrounds, 0 corrupt: 100%|██████████| 22/22 [00:00<00:00, 894.59it/s]" + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/generated_dataset_yolo/train/labels... 24 images, 0 backgrounds, 0 corrupt: 100%|██████████| 24/24 [00:00<00:00, 1156.29it/s]" ] }, { @@ -598,7 +2533,7 @@ "output_type": "stream", "text": [ "\n", - "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<00:00, 5119.17it/s]" + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<00:00, 610.38it/s]" ] }, { @@ -619,13 +2554,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Plotting labels to runs/segment/train4/labels.jpg... \n", + "Plotting labels to runs/segment/train/labels.jpg... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001429, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n", "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n", "Image sizes 640 train, 640 val\n", "Using 2 dataloader workers\n", - "Logging results to \u001b[1mruns/segment/train4\u001b[0m\n", + "Logging results to \u001b[1mruns/segment/train\u001b[0m\n", "Starting training for 200 epochs...\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" @@ -635,15 +2570,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 1/200 2.78G 0.794 3.071 3.092 1.307 18 640: 100%|██████████| 2/2 [00:02<00:00, 1.08s/it]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.57it/s]" + " 1/200 2.81G 0.9583 3.042 3.096 1.435 24 640: 100%|██████████| 2/2 [00:04<00:00, 2.23s/it]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.71it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0152 1 0.863 0.688 0.0152 1 0.863 0.595\n" + " all 3 3 0.0154 1 0.995 0.763 0.0154 1 0.995 0.758\n" ] }, { @@ -665,15 +2600,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 2/200 2.74G 0.9745 3.363 3.213 1.413 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.01it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.59it/s]" + " 2/200 2.73G 0.9505 2.288 3.178 1.424 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.016 1 0.83 0.665 0.016 1 0.83 0.615\n" + " all 3 3 0.0156 1 0.83 0.648 0.0156 1 0.83 0.626\n" ] }, { @@ -695,15 +2630,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 3/200 2.83G 0.9595 3.171 3.163 1.394 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.41it/s]" + " 3/200 2.83G 0.7481 2.647 3.072 1.264 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.70it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0149 1 0.995 0.813 0.0149 1 0.995 0.863\n" + " all 3 3 0.0135 1 0.913 0.706 0.0135 1 0.913 0.83\n" ] }, { @@ -725,15 +2660,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 4/200 2.76G 0.8419 2.756 3.12 1.332 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.42it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.01it/s]" + " 4/200 2.76G 0.7107 2.303 2.99 1.222 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.73it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0142 1 0.731 0.559 0.0142 1 0.731 0.658\n" + " all 3 3 0.012 1 0.72 0.593 0.012 1 0.72 0.598\n" ] }, { @@ -755,15 +2690,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 5/200 2.76G 0.7403 1.975 2.961 1.285 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.39it/s]" + " 5/200 2.78G 0.7299 1.833 2.877 1.219 29 640: 100%|██████████| 2/2 [00:00<00:00, 3.91it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.77it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0148 1 0.591 0.335 0.0148 1 0.618 0.482\n" + " all 3 3 0.0138 1 0.863 0.757 0.0138 1 0.863 0.794\n" ] }, { @@ -785,15 +2720,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 6/200 2.75G 0.5791 1.45 2.634 1.19 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.61it/s]" + " 6/200 2.78G 0.5273 1.158 2.513 1.083 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.41it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0168 1 0.641 0.376 0.0168 1 0.641 0.539\n" + " all 3 3 0.0169 1 0.863 0.757 0.0169 1 0.863 0.794\n" ] }, { @@ -815,15 +2750,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 7/200 2.76G 0.6529 1.048 2.372 1.132 19 640: 100%|██████████| 2/2 [00:01<00:00, 1.92it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.95it/s]\n" + " 7/200 2.78G 0.6557 1.336 2.353 1.261 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.54it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.0175 1 0.913 0.797 0.0175 1 0.913 0.814\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.019 1 0.624 0.402 0.019 1 0.624 0.546\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -832,15 +2780,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 8/200 2.73G 0.8078 1.601 2.268 1.313 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.37it/s]" + " 8/200 2.76G 0.6405 1.144 2.119 1.26 23 640: 100%|██████████| 2/2 [00:01<00:00, 1.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.32it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0208 1 0.83 0.593 0.0208 1 0.83 0.724\n" + " all 3 3 0.0199 1 0.995 0.895 0.0199 1 0.995 0.962\n" ] }, { @@ -862,15 +2810,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 9/200 2.76G 0.8734 1.444 2.121 1.336 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.73it/s]" + " 9/200 2.78G 0.7267 0.9904 1.839 1.238 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.35it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.47it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0224 1 0.863 0.638 0.0224 1 0.863 0.737\n" + " all 3 3 0.0194 1 0.995 0.841 0.0194 1 0.995 0.962\n" ] }, { @@ -892,15 +2840,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 10/200 2.78G 0.794 1.74 1.915 1.337 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.89it/s]" + " 10/200 2.78G 0.6091 0.7611 1.561 1.187 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0238 1 0.995 0.664 0.0238 1 0.995 0.786\n" + " all 3 3 0.0199 1 0.995 0.841 0.0199 1 0.995 0.962\n" ] }, { @@ -922,15 +2870,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 11/200 2.79G 0.8365 1.086 1.723 1.354 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.44it/s]" + " 11/200 2.78G 0.5849 0.9609 1.406 1.14 28 640: 100%|██████████| 2/2 [00:00<00:00, 3.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0242 1 0.995 0.751 0.0242 1 0.995 0.864\n" + " all 3 3 0.0192 1 0.995 0.858 0.0192 1 0.995 0.962\n" ] }, { @@ -952,15 +2900,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 12/200 2.76G 0.6774 1.02 1.762 1.155 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.23it/s]" + " 12/200 2.74G 0.5477 1.397 1.503 1.126 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.34it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0221 1 0.995 0.787 0.0221 1 0.995 0.94\n" + " all 3 3 0.0184 1 0.995 0.841 0.0184 1 0.995 0.962\n" ] }, { @@ -982,92 +2930,88 @@ "name": "stderr", "output_type": "stream", "text": [ - " 13/200 2.74G 0.6792 1.08 1.827 1.243 16 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.95it/s]\n" + " 13/200 2.76G 0.6779 0.7902 1.345 1.218 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.07it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0183 1 0.995 0.753 0.0183 1 0.995 0.94\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.0175 1 0.995 0.813 0.0175 1 0.995 0.962\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 14/200 2.75G 0.7153 0.8324 1.535 1.184 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.33it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0165 1 0.995 0.777 0.0165 1 0.995 0.962\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 14/200 2.76G 0.5625 0.8774 1.199 1.113 34 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.0163 1 0.995 0.813 0.0163 1 0.995 0.962\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 15/200 2.72G 0.691 1.133 1.724 1.273 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.44it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0141 1 0.995 0.763 0.0141 1 0.995 0.951\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 15/200 2.74G 0.7023 0.8258 1.409 1.316 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.0145 1 0.995 0.84 0.0145 1 0.995 0.962\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 16/200 2.74G 0.7734 1.09 1.697 1.259 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.10it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.33it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0124 1 0.995 0.73 0.0124 1 0.995 0.863\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1076,32 +3020,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 17/200 2.78G 0.8269 0.8279 1.658 1.273 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.51it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.80it/s]\n" + " 16/200 2.76G 0.697 0.6692 1.41 1.284 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.0562 1 0.995 0.752 0.0562 1 0.995 0.69\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.013 1 0.995 0.88 0.013 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 18/200 2.75G 0.7636 0.7614 1.539 1.301 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.43it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.333 1 0.995 0.802 0.333 1 0.995 0.587\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1110,15 +3050,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 19/200 2.78G 0.7204 0.9516 1.419 1.197 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.20it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.87it/s]" + " 17/200 2.79G 0.6502 1.052 1.293 1.191 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.65it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00974 1 0.995 0.785 0.00974 1 0.995 0.433\n" + " all 3 3 0.0121 1 0.995 0.686 0.0121 1 0.995 0.995\n" ] }, { @@ -1140,15 +3080,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 20/200 2.78G 0.6958 1.272 1.479 1.189 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.15it/s]" + " 18/200 2.76G 0.6758 0.6604 1.322 1.227 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00888 1 0.913 0.672 0.00888 1 0.747 0.408\n" + " all 3 3 0.0109 1 0.995 0.785 0.0109 1 0.995 0.995\n" ] }, { @@ -1170,15 +3110,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 21/200 2.78G 0.6758 0.7882 1.464 1.163 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.47it/s]" + " 19/200 2.78G 0.629 0.7494 1.193 1.182 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.84it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.68it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00826 1 0.753 0.563 0.00551 0.667 0.556 0.345\n" + " all 3 3 0.873 1 0.995 0.819 0.873 1 0.995 0.951\n" ] }, { @@ -1200,15 +3140,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 22/200 2.81G 0.931 0.8528 1.548 1.456 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.24it/s]" + " 20/200 2.8G 0.5509 0.8281 1.175 1.091 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.34it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00794 1 0.995 0.741 0.00794 1 0.995 0.731\n" + " all 3 3 0.969 1 0.995 0.763 0.969 1 0.995 0.896\n" ] }, { @@ -1230,28 +3170,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 23/200 2.76G 0.883 0.8969 1.455 1.36 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.53it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.43it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " all 3 3 0.00781 1 0.995 0.796 0.00781 1 0.995 0.895\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + " 21/200 2.8G 0.5286 0.746 1.088 1.079 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.96it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.95 1 0.995 0.741 0.95 1 0.995 0.904\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1260,15 +3187,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 24/200 2.74G 0.7606 0.711 1.423 1.274 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.12it/s]" + " 22/200 2.76G 0.7352 0.9631 1.335 1.246 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.00744 1 0.995 0.569 0.00744 1 0.995 0.895\n" + " all 3 3 0.939 1 0.995 0.764 0.939 1 0.995 0.895\n" ] }, { @@ -1290,15 +3217,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 25/200 2.76G 0.7699 0.7434 1.451 1.269 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.14it/s]" + " 23/200 2.78G 0.6152 0.738 1.216 1.092 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.197 1 0.995 0.569 0.197 1 0.995 0.887\n" + " all 3 3 0.968 1 0.995 0.863 0.968 1 0.995 0.83\n" ] }, { @@ -1320,15 +3247,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 26/200 2.78G 0.6797 0.7577 1.407 1.112 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.29it/s]" + " 24/200 2.76G 0.6584 0.7053 1.219 1.18 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.41it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.967 1 0.995 0.752 0.967 1 0.995 0.885\n" + " all 3 3 0.975 1 0.995 0.863 0.975 1 0.995 0.684\n" ] }, { @@ -1350,15 +3277,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 27/200 2.78G 0.5848 0.7211 1.409 1.127 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.46it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.81it/s]" + " 25/200 2.78G 0.6005 0.6082 1.197 1.148 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.39it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.976 1 0.995 0.84 0.976 1 0.995 0.929\n" + " all 3 3 0.975 1 0.995 0.847 0.975 1 0.995 0.676\n" ] }, { @@ -1380,15 +3307,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 28/200 2.77G 0.874 0.7393 1.373 1.308 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.93it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.31it/s]" + " 26/200 2.8G 0.6141 0.8144 1.342 1.114 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.38it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.82it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.976 1 0.995 0.84 0.976 1 0.995 0.929\n" + " all 3 3 0.964 1 0.995 0.83 0.964 1 0.995 0.808\n" ] }, { @@ -1410,15 +3337,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 29/200 2.76G 0.763 0.7376 1.367 1.298 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.03it/s]" + " 27/200 2.79G 0.677 0.5949 1.186 1.244 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.23it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.709 0.995 0.807 1 0.709 0.995 0.929\n" + " all 3 3 0.963 1 0.995 0.895 0.963 1 0.995 0.863\n" ] }, { @@ -1440,15 +3367,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 30/200 2.76G 0.7695 0.6632 1.434 1.225 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.29it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.34it/s]\n" + " 28/200 2.77G 0.5461 0.6246 1.188 1.064 30 640: 100%|██████████| 2/2 [00:00<00:00, 4.23it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.04it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.963 1 0.995 0.895 0.963 1 0.995 0.863\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.984 0.995 0.719 1 0.984 0.995 0.895\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1457,15 +3397,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 31/200 2.75G 0.7186 0.6687 1.374 1.205 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.71it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.17it/s]" + " 29/200 2.74G 0.5103 0.6436 1.228 1.104 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.81it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.984 0.995 0.719 1 0.984 0.995 0.895\n" + " all 3 3 0.941 1 0.995 0.895 0.941 1 0.995 0.912\n" ] }, { @@ -1487,15 +3427,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 32/200 2.78G 0.708 0.7252 1.366 1.183 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.87it/s]" + " 30/200 2.77G 0.6871 0.5663 1.134 1.259 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.12it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.708 0.979 1 0.995 0.912\n" + " all 3 3 0.979 1 0.995 0.885 0.979 1 0.995 0.962\n" ] }, { @@ -1517,58 +3457,62 @@ "name": "stderr", "output_type": "stream", "text": [ - " 33/200 2.78G 0.698 0.9198 1.265 1.175 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.97it/s]" + " 31/200 2.75G 0.6316 1.025 1.241 1.196 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.47it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.08it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.97 1 0.995 0.763 0.97 1 0.995 0.907\n" + " all 3 3 0.979 1 0.995 0.885 0.979 1 0.995 0.962\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 32/200 2.8G 0.5306 0.7407 1.11 1.06 31 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 1 1 0.995 0.852 1 1 0.995 0.912\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 34/200 2.73G 0.5212 0.6669 1.352 1.122 14 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.05it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.97 1 0.995 0.763 0.97 1 0.995 0.907\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 33/200 2.79G 0.6779 0.8553 1.202 1.222 30 640: 100%|██████████| 2/2 [00:00<00:00, 2.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.61it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 1 0.994 0.995 0.84 1 0.994 0.995 0.929\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -1577,15 +3521,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 35/200 2.76G 0.6894 0.8063 1.307 1.179 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.35it/s]" + " 34/200 2.81G 0.6393 0.6588 1.134 1.207 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.975 1 0.995 0.895 0.975 1 0.995 0.895\n" + " all 3 3 1 0.994 0.995 0.84 1 0.994 0.995 0.929\n" ] }, { @@ -1607,15 +3551,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 36/200 2.77G 0.6342 0.6469 1.179 1.086 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.50it/s]" + " 35/200 2.78G 0.5601 0.7836 1.093 1.099 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.34it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.53it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.969 1 0.995 0.857 0.969 1 0.995 0.797\n" + " all 3 3 0.983 1 0.995 0.895 0.983 1 0.995 0.912\n" ] }, { @@ -1637,15 +3581,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 37/200 2.77G 0.8963 1.439 1.324 1.326 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.54it/s]" + " 36/200 2.77G 0.6071 0.6343 1.098 1.144 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.42it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.969 1 0.995 0.857 0.969 1 0.995 0.797\n" + " all 3 3 0.798 1 0.995 0.786 0.798 1 0.995 0.863\n" ] }, { @@ -1667,15 +3611,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 38/200 2.77G 0.6453 0.5816 1.252 1.081 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.66it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.21it/s]" + " 37/200 2.77G 0.5417 0.501 1.046 1.054 34 640: 100%|██████████| 2/2 [00:00<00:00, 4.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.97 1 0.995 0.83 0.97 1 0.995 0.653\n" + " all 3 3 0.798 1 0.995 0.786 0.798 1 0.995 0.863\n" ] }, { @@ -1697,15 +3641,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 39/200 2.76G 0.7483 0.8435 1.437 1.208 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.90it/s]" + " 38/200 2.78G 0.6499 0.7335 1.172 1.12 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.665 0.913 0.772 1 0.665 0.913 0.666\n" + " all 3 3 0.981 1 0.995 0.863 0.981 1 0.995 0.912\n" ] }, { @@ -1727,15 +3671,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 40/200 2.77G 0.764 0.6151 1.333 1.262 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.64it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.00it/s]" + " 39/200 2.79G 0.5988 0.7146 1.003 1.156 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.665 0.913 0.772 1 0.665 0.913 0.666\n" + " all 3 3 0.985 1 0.995 0.852 0.985 1 0.995 0.907\n" ] }, { @@ -1757,15 +3701,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 41/200 2.78G 0.7048 1.037 1.218 1.114 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.53it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.53it/s]" + " 40/200 2.77G 0.6527 0.5838 1.083 1.122 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.46it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.559 1 0.83 0.698 0.559 1 0.83 0.693\n" + " all 3 3 0.985 1 0.995 0.852 0.985 1 0.995 0.907\n" ] }, { @@ -1787,15 +3731,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 42/200 2.8G 0.6763 0.695 1.257 1.141 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.07it/s]" + " 41/200 2.8G 0.5489 0.5629 0.9834 1.037 34 640: 100%|██████████| 2/2 [00:00<00:00, 4.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.85it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.486 1 0.753 0.636 0.486 1 0.753 0.658\n" + " all 3 3 0.987 1 0.995 0.852 0.987 1 0.995 0.907\n" ] }, { @@ -1817,15 +3761,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 43/200 2.76G 0.7237 0.7255 1.244 1.193 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.97it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.96it/s]" + " 42/200 2.79G 0.5075 0.5155 0.9245 1.062 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.41it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.05it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.486 1 0.753 0.636 0.486 1 0.753 0.658\n" + " all 3 3 0.987 1 0.995 0.752 0.987 1 0.995 0.907\n" ] }, { @@ -1847,15 +3791,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 44/200 2.75G 0.8198 1.073 1.391 1.228 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.89it/s]" + " 43/200 2.77G 0.7016 0.7532 1.123 1.243 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.727 1 0.913 0.722 0.727 1 0.913 0.821\n" + " all 3 3 0.987 1 0.995 0.752 0.987 1 0.995 0.907\n" ] }, { @@ -1877,15 +3821,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 45/200 2.77G 0.8204 1.331 1.545 1.275 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.54it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.05it/s]" + " 44/200 2.77G 0.703 0.6425 1.191 1.184 31 640: 100%|██████████| 2/2 [00:00<00:00, 2.35it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.75it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.727 1 0.913 0.722 0.727 1 0.913 0.821\n" + " all 3 3 0.986 1 0.995 0.676 0.986 1 0.995 0.907\n" ] }, { @@ -1907,15 +3851,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 46/200 2.79G 0.6875 0.8194 1.49 1.173 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.80it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.27it/s]" + " 45/200 2.77G 0.6144 0.7645 1.084 1.127 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.92it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.44it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.979 0.995 0.686 1 0.979 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.676 0.986 1 0.995 0.907\n" ] }, { @@ -1937,15 +3881,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 47/200 2.75G 0.8966 1.198 1.514 1.326 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.97it/s]" + " 46/200 2.75G 0.595 0.5509 0.9787 1.164 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.36it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.11it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.979 0.995 0.686 1 0.979 0.995 0.962\n" + " all 3 3 0.994 1 0.995 0.75 0.994 1 0.995 0.871\n" ] }, { @@ -1967,15 +3911,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 48/200 2.76G 0.586 0.8328 1.18 1.214 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.69it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.52it/s]" + " 47/200 2.77G 0.7183 0.6334 1.258 1.256 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.17it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.57it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.653 0.979 1 0.995 0.995\n" + " all 3 3 0.994 1 0.995 0.75 0.994 1 0.995 0.871\n" ] }, { @@ -1997,15 +3941,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 49/200 2.77G 0.6485 0.5949 1.183 1.118 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.03it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.61it/s]\n" + " 48/200 2.79G 0.6762 0.5441 1.106 1.161 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.764 0.987 1 0.995 0.847\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.653 0.979 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2014,15 +3971,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 50/200 2.77G 0.7331 1.04 1.334 1.242 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.38it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.98it/s]" + " 49/200 2.79G 0.6396 0.7419 1.03 1.112 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.40it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.896 0.985 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.764 0.987 1 0.995 0.847\n" ] }, { @@ -2044,15 +4001,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 51/200 2.74G 0.7259 0.8039 1.293 1.195 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.86it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.63it/s]" + " 50/200 2.77G 0.6097 0.8468 1.127 1.135 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.66it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.896 0.985 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.797 0.986 1 0.995 0.895\n" ] }, { @@ -2074,15 +4031,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 52/200 2.79G 0.7508 0.9683 1.539 1.202 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.30it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.80it/s]" + " 51/200 2.75G 0.7059 0.5626 1.164 1.185 30 640: 100%|██████████| 2/2 [00:00<00:00, 4.11it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.982 1 0.995 0.912 0.982 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.797 0.986 1 0.995 0.895\n" ] }, { @@ -2104,15 +4061,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 53/200 2.75G 0.7076 0.6069 1.266 1.159 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.47it/s]" + " 52/200 2.79G 0.5749 0.6226 1.039 1.095 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.12it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.13it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.982 1 0.995 0.912 0.982 1 0.995 0.962\n" + " all 3 3 0.981 1 0.995 0.642 0.981 1 0.995 0.593\n" ] }, { @@ -2134,15 +4091,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 54/200 2.75G 0.7378 0.7723 1.246 1.216 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.95it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.44it/s]" + " 53/200 2.79G 0.5441 0.641 1.005 1.096 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.41it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 0.981 1 0.995 0.642 0.981 1 0.995 0.593\n" ] }, { @@ -2164,15 +4121,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 55/200 2.75G 0.6865 0.8221 1.171 1.182 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.42it/s]" + " 54/200 2.77G 0.6492 0.615 1.143 1.237 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.99it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.24it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 1 0.983 0.995 0.645 1 0.983 0.995 0.355\n" ] }, { @@ -2194,15 +4151,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 56/200 2.75G 0.5388 0.8311 1.103 1.099 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.52it/s]" + " 55/200 2.77G 0.6034 0.7347 1.062 1.081 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.78it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + " all 3 3 1 0.983 0.995 0.645 1 0.983 0.995 0.355\n" ] }, { @@ -2224,15 +4181,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 57/200 2.79G 0.8337 0.8106 1.37 1.229 18 640: 100%|██████████| 2/2 [00:00<00:00, 4.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.24it/s]\n" + " 56/200 2.79G 0.6689 0.7051 1.118 1.134 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.78it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 1 0.951 0.995 0.863 1 0.951 0.995 0.598\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2241,15 +4211,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 58/200 2.75G 0.8342 0.7238 1.28 1.227 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.52it/s]" + " 57/200 2.81G 0.5432 0.6506 0.8974 1.055 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.59it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.973 1 0.995 0.83 0.973 1 0.995 0.912\n" + " all 3 3 1 0.951 0.995 0.863 1 0.951 0.995 0.598\n" ] }, { @@ -2271,15 +4241,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 59/200 2.75G 0.6904 0.7557 1.099 1.099 26 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.35it/s]" + " 58/200 2.77G 0.4963 0.5577 0.9065 1.031 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.47it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.973 1 0.995 0.83 0.973 1 0.995 0.912\n" + " all 3 3 1 0.977 0.995 0.863 1 0.977 0.995 0.266\n" ] }, { @@ -2301,32 +4271,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 60/200 2.75G 0.8214 0.8577 1.239 1.26 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.75it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.45it/s]\n" + " 59/200 2.77G 0.612 0.7789 1.068 1.168 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.79it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.994 1 0.995 0.735 0.994 1 0.995 0.885\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 1 0.977 0.995 0.863 1 0.977 0.995 0.266\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 61/200 2.75G 0.7685 0.5507 1.197 1.217 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.91it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.14it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.994 1 0.995 0.735 0.994 1 0.995 0.885\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2335,15 +4301,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 62/200 2.76G 0.867 0.7007 1.298 1.374 14 640: 100%|██████████| 2/2 [00:00<00:00, 2.17it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.37it/s]" + " 60/200 2.77G 0.5792 0.6189 0.9685 1.121 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.07it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.22it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.992 1 0.995 0.84 0.992 1 0.995 0.912\n" + " all 3 3 1 0.991 0.995 0.895 0.663 0.667 0.556 0.0556\n" ] }, { @@ -2365,15 +4331,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 63/200 2.75G 0.7329 0.6407 1.103 1.182 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.39it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.86it/s]" + " 61/200 2.79G 0.6452 0.752 1.03 1.142 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.19it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.992 1 0.995 0.84 0.992 1 0.995 0.912\n" + " all 3 3 1 0.991 0.995 0.895 0.663 0.667 0.556 0.0556\n" ] }, { @@ -2395,15 +4361,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 64/200 2.77G 0.7402 0.809 1.136 1.125 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.90it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.33it/s]" + " 62/200 2.79G 0.6621 0.5832 1.21 1.145 19 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.96it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.983 0.995 0.807 1 0.983 0.995 0.929\n" + " all 3 3 0.993 1 0.995 0.929 0.993 1 0.995 0.444\n" ] }, { @@ -2425,15 +4391,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 65/200 2.76G 0.5545 0.8058 1.04 1.115 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.99it/s]" + " 63/200 2.79G 0.6145 0.846 1.018 1.134 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.13it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.63it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 1 0.983 0.995 0.807 1 0.983 0.995 0.929\n" + " all 3 3 0.993 1 0.995 0.929 0.993 1 0.995 0.444\n" ] }, { @@ -2455,15 +4421,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 66/200 2.75G 0.888 0.7873 1.457 1.297 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.62it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.26it/s]" + " 64/200 2.79G 0.6153 0.4738 1.005 1.195 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.99 1 0.995 0.818 0.99 1 0.995 0.995\n" + " all 3 3 0.994 1 0.995 0.929 0.994 1 0.995 0.687\n" ] }, { @@ -2485,15 +4451,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 67/200 2.77G 0.7276 0.6194 1.098 1.107 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.64it/s]" + " 65/200 2.77G 0.5882 0.5018 0.8927 1.172 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.33it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.04it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.99 1 0.995 0.818 0.99 1 0.995 0.995\n" + " all 3 3 0.994 1 0.995 0.929 0.994 1 0.995 0.687\n" ] }, { @@ -2515,15 +4481,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 68/200 2.75G 0.5356 0.8722 1.035 1.008 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.21it/s]" + " 66/200 2.77G 0.6998 0.7637 1.222 1.209 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.92it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.12it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.713 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.907 0.983 1 0.995 0.863\n" ] }, { @@ -2545,15 +4511,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 69/200 2.75G 0.7544 0.7592 1.393 1.254 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.92it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.93it/s]" + " 67/200 2.79G 0.6309 0.8498 0.9978 1.145 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.01it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.27it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.713 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.907 0.983 1 0.995 0.863\n" ] }, { @@ -2575,15 +4541,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 70/200 2.75G 0.6781 0.7027 1.047 1.137 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.67it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.43it/s]" + " 68/200 2.77G 0.6761 0.588 1.082 1.221 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.63it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.983 1 0.995 0.697 0.983 1 0.995 0.863\n" + " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.907\n" ] }, { @@ -2605,15 +4571,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 71/200 2.76G 0.5566 0.5015 1.118 1.096 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.36it/s]" + " 69/200 2.77G 0.6545 0.6614 1.048 1.126 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.44it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.983 1 0.995 0.697 0.983 1 0.995 0.863\n" + " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.907\n" ] }, { @@ -2635,15 +4601,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 72/200 2.81G 0.7059 0.5541 1.327 1.252 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.29it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.12it/s]" + " 70/200 2.77G 0.5807 0.634 0.9454 1.079 31 640: 100%|██████████| 2/2 [00:00<00:00, 3.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.97it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.924 1 0.995 0.682 0.924 1 0.995 0.832\n" + " all 3 3 0.985 1 0.995 0.863 0.985 1 0.995 0.962\n" ] }, { @@ -2665,15 +4631,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 73/200 2.76G 0.6503 0.4222 1.137 1.099 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.99it/s]" + " 71/200 2.77G 0.6684 0.4863 0.9834 1.231 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.12it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.924 1 0.995 0.682 0.924 1 0.995 0.832\n" + " all 3 3 0.985 1 0.995 0.863 0.985 1 0.995 0.962\n" ] }, { @@ -2695,15 +4661,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 74/200 2.77G 0.613 0.597 1.154 1.098 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.23it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.63it/s]" + " 72/200 2.77G 0.5383 0.4674 0.9989 1.108 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.90it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.799 1 0.995 0.697 0.799 1 0.995 0.83\n" + " all 3 3 0.97 1 0.995 0.929 0.97 1 0.995 0.94\n" ] }, { @@ -2725,15 +4691,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 75/200 2.77G 0.7407 0.739 1.281 1.161 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.24it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.19it/s]" + " 73/200 2.79G 0.751 0.6377 1.158 1.17 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.25it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.799 1 0.995 0.697 0.799 1 0.995 0.83\n" + " all 3 3 0.97 1 0.995 0.929 0.97 1 0.995 0.94\n" ] }, { @@ -2755,15 +4721,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 76/200 2.75G 0.7392 0.7957 1.107 1.196 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.68it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.56it/s]" + " 74/200 2.79G 0.6371 0.5049 0.8836 1.152 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.32it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.974 1 0.995 0.73 0.974 1 0.995 0.863\n" + " all 3 3 0.975 1 0.995 0.895 0.975 1 0.995 0.94\n" ] }, { @@ -2785,15 +4751,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 77/200 2.77G 0.5959 0.6653 0.9648 1.081 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.29it/s]" + " 75/200 2.75G 0.7281 0.7069 1.018 1.197 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.68it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.974 1 0.995 0.73 0.974 1 0.995 0.863\n" + " all 3 3 0.975 1 0.995 0.895 0.975 1 0.995 0.94\n" ] }, { @@ -2815,15 +4781,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 78/200 2.77G 0.6506 0.5285 1.045 1.119 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.30it/s]" + " 76/200 2.77G 0.7603 0.9357 1.084 1.261 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.00it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.39it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.895 0.983 1 0.995 0.94\n" ] }, { @@ -2845,15 +4811,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 79/200 2.81G 0.6239 0.588 1.036 1.048 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.20it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.74it/s]" + " 77/200 2.79G 0.5921 0.5092 0.8859 1.061 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.20it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.51it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.895\n" + " all 3 3 0.983 1 0.995 0.895 0.983 1 0.995 0.94\n" ] }, { @@ -2875,15 +4841,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 80/200 2.75G 0.6568 0.7361 1.037 1.155 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.16it/s]" + " 78/200 2.79G 0.577 0.4941 0.9087 1.093 28 640: 100%|██████████| 2/2 [00:00<00:00, 3.43it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.11it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.83 0.986 1 0.995 0.962\n" + " all 3 3 0.984 1 0.995 0.895 0.984 1 0.995 0.995\n" ] }, { @@ -2905,15 +4871,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 81/200 2.75G 0.6159 0.588 1.059 1.104 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.04it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.66it/s]" + " 79/200 2.77G 0.7406 0.5359 1.127 1.164 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.15it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.83 0.986 1 0.995 0.962\n" + " all 3 3 0.984 1 0.995 0.895 0.984 1 0.995 0.995\n" ] }, { @@ -2935,28 +4901,32 @@ "name": "stderr", "output_type": "stream", "text": [ - " 82/200 2.73G 0.7803 0.5306 1.307 1.284 13 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.67it/s]" + " 80/200 2.79G 0.7346 0.6198 1.153 1.306 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.07it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.714 0.987 1 0.995 0.962\n" + " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 81/200 2.75G 0.6564 0.598 1.097 1.137 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.13it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.71it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.984 1 0.995 0.863 0.984 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -2965,15 +4935,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 83/200 2.75G 0.7378 0.7282 1.307 1.223 11 640: 100%|██████████| 2/2 [00:00<00:00, 2.65it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.05it/s]" + " 82/200 2.78G 0.6806 0.747 1.023 1.159 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.92it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.30it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.714 0.987 1 0.995 0.962\n" + " all 3 3 0.985 1 0.995 0.83 0.985 1 0.995 0.995\n" ] }, { @@ -2995,15 +4965,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 84/200 2.79G 0.7356 0.5783 1.145 1.187 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.43it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.41it/s]" + " 83/200 2.77G 0.6687 0.6102 0.9566 1.172 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.38it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.87it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.747 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.83 0.985 1 0.995 0.995\n" ] }, { @@ -3025,15 +4995,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 85/200 2.75G 0.7554 0.5599 1.178 1.238 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.48it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.14it/s]" + " 84/200 2.79G 0.7126 0.6014 0.968 1.171 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.04it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.747 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.847 0.985 1 0.995 0.995\n" ] }, { @@ -3055,15 +5025,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 86/200 2.8G 0.689 0.674 1.013 1.138 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.94it/s]\n" + " 85/200 2.77G 0.5952 0.6163 0.9467 1.089 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.55it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.07it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.985 1 0.995 0.847 0.985 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -3072,15 +5055,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 87/200 2.77G 0.7633 0.7658 1.06 1.244 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.03it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.43it/s]" + " 86/200 2.77G 0.4459 0.4924 0.8475 1.055 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n" + " all 3 3 0.937 1 0.995 0.879 0.937 1 0.995 0.995\n" ] }, { @@ -3102,15 +5085,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 88/200 2.77G 0.6823 0.4675 1.017 1.217 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.36it/s]" + " 87/200 2.79G 0.5956 0.6826 1.022 1.156 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.995\n" + " all 3 3 0.937 1 0.995 0.879 0.937 1 0.995 0.995\n" ] }, { @@ -3132,15 +5115,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 89/200 2.79G 0.5818 0.5296 0.9357 1.035 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.15it/s]" + " 88/200 2.79G 0.5529 0.6333 0.8344 1.034 32 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.995\n" + " all 3 3 0.929 1 0.995 0.863 0.929 1 0.995 0.995\n" ] }, { @@ -3162,15 +5145,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 90/200 2.8G 0.6256 0.4748 1.006 1.125 13 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.03it/s]" + " 89/200 2.81G 0.5817 0.6426 0.8719 1.108 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.52it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.06it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.94\n" + " all 3 3 0.929 1 0.995 0.863 0.929 1 0.995 0.995\n" ] }, { @@ -3192,15 +5175,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 91/200 2.75G 0.6188 0.5238 1.121 1.126 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.52it/s]" + " 90/200 2.77G 0.5982 0.5639 0.8665 1.144 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.69it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.852 0.986 1 0.995 0.94\n" + " all 3 3 0.873 1 0.995 0.83 0.873 1 0.995 0.995\n" ] }, { @@ -3222,15 +5205,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 92/200 2.75G 0.6475 0.5957 1.019 1.118 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.77it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.53it/s]" + " 91/200 2.75G 0.6225 0.5776 1.015 1.157 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.80it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.84 0.986 1 0.995 0.895\n" + " all 3 3 0.873 1 0.995 0.83 0.873 1 0.995 0.995\n" ] }, { @@ -3252,15 +5235,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 93/200 2.79G 0.5474 1.04 0.8371 1.03 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.58it/s]" + " 92/200 2.77G 0.4875 0.3284 0.7068 1.109 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.28it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.58it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.84 0.986 1 0.995 0.895\n" + " all 3 3 0.975 1 0.995 0.731 0.975 1 0.995 0.885\n" ] }, { @@ -3282,15 +5265,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 94/200 2.75G 0.6746 0.6793 1.066 1.171 13 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.88it/s]" + " 93/200 2.79G 0.5405 0.5058 0.8203 1.12 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.76it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.73it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.895\n" + " all 3 3 0.975 1 0.995 0.731 0.975 1 0.995 0.885\n" ] }, { @@ -3312,15 +5295,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 95/200 2.75G 0.66 0.7117 0.9496 1.131 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.45it/s]" + " 94/200 2.79G 0.5286 0.5116 0.804 1.055 31 640: 100%|██████████| 2/2 [00:00<00:00, 2.40it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.895\n" + " all 3 3 0.967 1 0.995 0.814 0.967 1 0.995 0.962\n" ] }, { @@ -3342,15 +5325,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 96/200 2.77G 0.669 0.4866 1.044 1.193 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.30it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.30it/s]" + " 95/200 2.79G 0.4865 0.4779 0.7034 1.03 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.10it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.78it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.852\n" + " all 3 3 0.967 1 0.995 0.814 0.967 1 0.995 0.962\n" ] }, { @@ -3372,15 +5355,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 97/200 2.8G 0.5996 0.4582 0.9039 1.154 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.25it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.25it/s]" + " 96/200 2.81G 0.4951 0.5934 0.816 0.9905 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.07it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.968 1 0.995 0.84 0.968 1 0.995 0.852\n" + " all 3 3 0.98 1 0.995 0.896 0.98 1 0.995 0.962\n" ] }, { @@ -3402,15 +5385,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 98/200 2.77G 0.6977 1.052 1.111 1.195 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.36it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.64it/s]" + " 97/200 2.79G 0.5855 0.5365 0.8842 1.054 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.971 1 0.995 0.852 0.971 1 0.995 0.94\n" + " all 3 3 0.98 1 0.995 0.896 0.98 1 0.995 0.962\n" ] }, { @@ -3432,15 +5415,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 99/200 2.77G 0.5432 0.5559 0.8922 1.064 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.37it/s]" + " 98/200 2.79G 0.5104 0.4402 0.7637 1.067 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.13it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.59it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.971 1 0.995 0.852 0.971 1 0.995 0.94\n" + " all 3 3 0.983 1 0.995 0.896 0.983 1 0.995 0.962\n" ] }, { @@ -3462,15 +5445,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 100/200 2.82G 0.6063 0.5036 1.015 1.061 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.65it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.18it/s]" + " 99/200 2.76G 0.6647 0.5047 0.9825 1.166 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.51it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.68it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.895 0.979 1 0.995 0.94\n" + " all 3 3 0.983 1 0.995 0.896 0.983 1 0.995 0.962\n" ] }, { @@ -3492,15 +5475,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 101/200 2.75G 0.7022 0.5224 1.057 1.146 16 640: 100%|██████████| 2/2 [00:00<00:00, 4.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.99it/s]" + " 100/200 2.77G 0.4676 0.4558 0.7198 0.983 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.55it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.979 1 0.995 0.895 0.979 1 0.995 0.94\n" + " all 3 3 0.987 1 0.995 0.879 0.987 1 0.995 0.951\n" ] }, { @@ -3522,15 +5505,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 102/200 2.77G 0.6843 0.51 0.9352 1.141 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.26it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.38it/s]" + " 101/200 2.79G 0.6673 0.6273 0.9181 1.15 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.863 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.879 0.987 1 0.995 0.951\n" ] }, { @@ -3552,15 +5535,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 103/200 2.75G 0.6863 0.5164 1.049 1.209 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.82it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.32it/s]" + " 102/200 2.79G 0.4351 0.4311 0.7462 1.008 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.863 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -3582,15 +5565,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 104/200 2.79G 0.566 0.4932 0.8738 1.07 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.85it/s]" + " 103/200 2.77G 0.4994 0.6399 0.7985 1.061 30 640: 100%|██████████| 2/2 [00:00<00:00, 2.62it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.756 0.986 1 0.995 0.929\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -3612,15 +5595,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 105/200 2.75G 0.6416 0.8384 1.06 1.14 14 640: 100%|██████████| 2/2 [00:00<00:00, 4.67it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.55it/s]" + " 104/200 2.77G 0.5192 0.6322 0.7951 1.058 29 640: 100%|██████████| 2/2 [00:00<00:00, 2.52it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.35it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.756 0.986 1 0.995 0.929\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3642,28 +5625,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 106/200 2.77G 0.6143 0.5127 0.9753 1.151 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.66it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " all 3 3 0.987 1 0.995 0.758 0.987 1 0.995 0.995\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + " 105/200 2.77G 0.6023 0.6443 0.8863 1.125 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.72it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.49it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -3672,15 +5642,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 107/200 2.78G 0.7773 1.029 1.161 1.252 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.78it/s]" + " 106/200 2.79G 0.5844 0.6149 0.8273 1.087 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.19it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.42it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.758 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3702,15 +5672,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 108/200 2.75G 0.533 0.4626 0.9247 1.048 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.19it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.66it/s]" + " 107/200 2.81G 0.5319 0.5602 0.829 1.03 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.84it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3732,15 +5702,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 109/200 2.77G 0.6707 0.4935 0.9969 1.179 12 640: 100%|██████████| 2/2 [00:00<00:00, 2.39it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.43it/s]" + " 108/200 2.77G 0.5152 0.4653 0.9078 1.096 18 640: 100%|██████████| 2/2 [00:00<00:00, 4.19it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.35it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3762,15 +5732,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 110/200 2.77G 0.6729 0.4916 1.083 1.114 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.37it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.35it/s]" + " 109/200 2.79G 0.456 0.5731 0.7045 1.022 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.48it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -3792,15 +5762,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 111/200 2.78G 0.6963 0.5948 1.017 1.136 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.47it/s]" + " 110/200 2.79G 0.4688 0.4529 0.7285 0.9873 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -3822,15 +5792,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 112/200 2.75G 0.6316 0.4456 0.9924 1.079 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.89it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.60it/s]" + " 111/200 2.81G 0.5863 0.627 0.87 1.082 27 640: 100%|██████████| 2/2 [00:00<00:00, 2.62it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.97it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.852 0.987 1 0.995 0.962\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -3852,15 +5822,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 113/200 2.77G 0.5224 0.4967 0.8122 1.025 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.23it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.08it/s]" + " 112/200 2.77G 0.474 0.5144 0.7142 1.079 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.77it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.26it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.852 0.987 1 0.995 0.962\n" + " all 3 3 0.985 1 0.995 0.962 0.985 1 0.995 0.995\n" ] }, { @@ -3882,15 +5852,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 114/200 2.73G 0.5403 0.4416 0.9397 1.09 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.60it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.60it/s]" + " 113/200 2.79G 0.5502 0.6755 0.8016 1.046 29 640: 100%|██████████| 2/2 [00:00<00:00, 2.75it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.83it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.962 0.985 1 0.995 0.995\n" ] }, { @@ -3912,15 +5882,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 115/200 2.75G 0.5727 0.502 0.8876 1.146 13 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.51it/s]" + " 114/200 2.75G 0.6288 0.4798 0.7952 1.149 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.863 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n" ] }, { @@ -3942,58 +5912,62 @@ "name": "stderr", "output_type": "stream", "text": [ - " 116/200 2.76G 0.5485 0.5809 0.9535 1.065 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.76it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.18it/s]" + " 115/200 2.77G 0.5762 0.5305 0.8851 1.117 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.47it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.99it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.885 0.987 1 0.995 0.995\n" + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 116/200 2.81G 0.5338 0.4947 0.7355 1.041 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.25it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.61it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 117/200 2.75G 0.4955 0.4099 0.8349 1.024 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.90it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.885 0.987 1 0.995 0.995\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 117/200 2.77G 0.4463 0.6745 0.7318 1.009 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.84it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.985 1 0.995 0.912 0.985 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4002,15 +5976,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 118/200 2.75G 0.5923 0.6652 0.9217 1.051 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.51it/s]" + " 118/200 2.75G 0.6975 0.5969 0.9205 1.25 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { @@ -4032,62 +6006,58 @@ "name": "stderr", "output_type": "stream", "text": [ - " 119/200 2.77G 0.609 0.4595 0.8211 1.128 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.58it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.09it/s]\n" + " 119/200 2.77G 0.443 0.4898 0.7086 1.021 27 640: 100%|██████████| 2/2 [00:00<00:00, 4.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.92it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 120/200 2.75G 0.6231 0.5781 1.006 1.122 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.99it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.79it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 120/200 2.77G 0.5391 0.5614 0.8701 1.04 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.57it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 121/200 2.77G 0.5762 0.9087 0.905 1.08 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.69it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.65it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4096,15 +6066,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 122/200 2.75G 0.5714 0.4716 0.8216 1.058 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.68it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.20it/s]" + " 121/200 2.79G 0.4734 0.5794 0.7894 1.062 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.30it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.995\n" ] }, { @@ -4126,15 +6096,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 123/200 2.75G 0.5502 0.5089 0.852 1.079 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.95it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.41it/s]" + " 122/200 2.77G 0.5546 0.4944 0.8185 1.072 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.84it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4156,15 +6126,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 124/200 2.75G 0.6137 0.4057 0.7874 1.09 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.77it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.43it/s]" + " 123/200 2.79G 0.5637 0.5521 0.8 1.047 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.49it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4186,15 +6156,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 125/200 2.82G 0.5204 0.5034 0.7814 0.9942 22 640: 100%|██████████| 2/2 [00:00<00:00, 4.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.62it/s]" + " 124/200 2.77G 0.4574 0.5327 0.7591 1.037 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.19it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.81it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.907 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4216,15 +6186,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 126/200 2.77G 0.6097 0.7312 0.8403 1.094 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.64it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.20it/s]" + " 125/200 2.75G 0.4458 0.5522 0.6966 0.9878 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.24it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4246,15 +6216,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 127/200 2.79G 0.492 0.4076 0.7375 0.9588 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.14it/s]" + " 126/200 2.79G 0.4334 0.479 0.7063 1.003 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.56it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.12it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4276,62 +6246,58 @@ "name": "stderr", "output_type": "stream", "text": [ - " 128/200 2.77G 0.5917 0.8382 0.9271 1.036 18 640: 100%|██████████| 2/2 [00:00<00:00, 4.01it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.81it/s]\n" + " 127/200 2.79G 0.4773 0.5007 0.6538 1.06 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.85it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.01it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n", - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 129/200 2.77G 0.5816 0.5468 0.9033 1.091 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.32it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.962\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 128/200 2.77G 0.56 0.5056 0.7226 1.053 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.28it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.37it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 130/200 2.77G 0.6392 0.4588 0.7785 1.094 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.91it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.962\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4340,15 +6306,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 131/200 2.79G 0.5477 0.4129 0.8522 1.083 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.08it/s]" + " 129/200 2.79G 0.4384 0.4542 0.6742 1.021 30 640: 100%|██████████| 2/2 [00:00<00:00, 3.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.962 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4370,15 +6336,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 132/200 2.75G 0.6358 0.5506 1.036 1.174 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.58it/s]" + " 130/200 2.77G 0.7 0.7683 0.8775 1.19 27 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4400,15 +6366,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 133/200 2.75G 0.5177 0.4507 0.809 1.027 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.00it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.87it/s]" + " 131/200 2.79G 0.4507 0.3894 0.7 1.014 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.20it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.10it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4430,15 +6396,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 134/200 2.75G 0.5144 0.4483 0.8161 1.095 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.31it/s]" + " 132/200 2.77G 0.5543 0.6216 0.8271 1.096 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.05it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4460,15 +6426,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 135/200 2.71G 0.5643 0.4754 0.9486 1.186 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.22it/s]" + " 133/200 2.79G 0.505 0.438 0.6984 1.066 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.73it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -4490,15 +6456,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 136/200 2.75G 0.6034 0.5004 0.8993 1.066 20 640: 100%|██████████| 2/2 [00:00<00:00, 3.52it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.81it/s]" + " 134/200 2.75G 0.4711 0.5895 0.7541 1.037 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.00it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.85it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.895 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4520,15 +6486,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 137/200 2.77G 0.5833 0.4331 0.7557 1.046 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.54it/s]" + " 135/200 2.75G 0.6022 0.4884 0.8812 1.173 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.37it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.895 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4550,15 +6516,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 138/200 2.76G 0.4614 0.3644 0.7709 0.9599 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.88it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.90it/s]" + " 136/200 2.77G 0.4984 0.4358 0.7416 1.055 27 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.33it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4580,15 +6546,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 139/200 2.75G 0.514 0.7363 0.7647 1.032 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.70it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.03it/s]" + " 137/200 2.79G 0.5259 0.6168 0.7836 1.061 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.41it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.912 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n" ] }, { @@ -4610,15 +6576,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 140/200 2.77G 0.6003 0.7076 0.959 1.12 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.38it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.36it/s]" + " 138/200 2.81G 0.4485 0.3906 0.6376 0.9531 24 640: 100%|██████████| 2/2 [00:00<00:00, 2.68it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4640,75 +6606,75 @@ "name": "stderr", "output_type": "stream", "text": [ - " 141/200 2.77G 0.6165 0.6826 0.918 1.054 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.09it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.96it/s]" + " 139/200 2.77G 0.4332 0.4712 0.6937 1.004 25 640: 100%|██████████| 2/2 [00:00<00:00, 2.88it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.30it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 140/200 2.79G 0.4546 0.3461 0.7288 1.014 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.51it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.14it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 142/200 2.8G 0.6499 0.604 0.8206 1.139 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.64it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.47it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 141/200 2.77G 0.4839 0.5361 0.7684 1.086 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.43it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 143/200 2.75G 0.4709 0.3645 0.8066 1.069 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.35it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.68it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4717,15 +6683,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 144/200 2.74G 0.5932 0.4871 0.817 1.147 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.26it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.49it/s]" + " 142/200 2.77G 0.5427 0.6068 0.7644 1.011 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.03it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.94 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4747,15 +6713,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 145/200 2.75G 0.5792 0.4397 0.795 1.094 14 640: 100%|██████████| 2/2 [00:00<00:00, 3.56it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.65it/s]" + " 143/200 2.77G 0.533 1.119 0.7007 1.11 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.20it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.94 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4777,15 +6743,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 146/200 2.76G 0.5253 0.5255 0.8925 1.031 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.55it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.37it/s]" + " 144/200 2.77G 0.494 0.4664 0.6727 1.047 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4807,15 +6773,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 147/200 2.78G 0.555 0.6465 0.7055 1.026 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.41it/s]" + " 145/200 2.77G 0.482 0.4516 0.7589 1.085 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.94 0.987 1 0.995 0.995\n" ] }, { @@ -4837,15 +6803,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 148/200 2.77G 0.5269 0.4604 0.8034 1.031 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.07it/s]" + " 146/200 2.79G 0.4651 0.5891 0.6903 0.9894 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4867,15 +6833,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 149/200 2.75G 0.4203 0.3653 0.6666 1.038 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.02it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.25it/s]" + " 147/200 2.79G 0.5923 0.4466 0.7914 1.14 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.27it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.02it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4897,15 +6863,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 150/200 2.75G 0.4719 0.348 0.7072 1.003 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.36it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.07it/s]" + " 148/200 2.79G 0.5587 0.54 0.8782 1.144 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.01it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4927,15 +6893,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 151/200 2.76G 0.4813 0.6396 0.686 1.036 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.88it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.80it/s]" + " 149/200 2.79G 0.4177 0.4314 0.6664 1.005 34 640: 100%|██████████| 2/2 [00:00<00:00, 3.71it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.32it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n" ] }, { @@ -4957,28 +6923,32 @@ "name": "stderr", "output_type": "stream", "text": [ - " 152/200 2.77G 0.4607 0.3515 0.6976 1.038 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.16it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.91it/s]" + " 150/200 2.77G 0.4813 0.5917 0.6691 1.007 35 640: 100%|██████████| 2/2 [00:00<00:00, 2.97it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.49it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + " 151/200 2.79G 0.5508 0.4525 0.764 1.132 29 640: 100%|██████████| 2/2 [00:00<00:00, 2.64it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.85it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + " all 3 3 0.987 1 0.995 0.951 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -4987,15 +6957,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 153/200 2.76G 0.4833 0.3946 0.7209 0.9901 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.69it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.72it/s]" + " 152/200 2.77G 0.4312 0.5333 0.6529 1.013 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.69it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -5017,15 +6987,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 154/200 2.75G 0.4215 0.4154 0.6492 0.9648 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.45it/s]" + " 153/200 2.77G 0.5734 0.5148 0.6719 1.138 24 640: 100%|██████████| 2/2 [00:00<00:00, 3.41it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.34it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -5047,15 +7017,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 155/200 2.73G 0.6319 0.4829 0.9325 1.183 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.42it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.13it/s]" + " 154/200 2.79G 0.4127 0.5978 0.6627 0.9756 25 640: 100%|██████████| 2/2 [00:00<00:00, 3.86it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.45it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" ] }, { @@ -5077,15 +7047,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 156/200 2.74G 0.4604 0.4138 0.7124 0.9908 17 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.52it/s]\n" + " 155/200 2.77G 0.4746 0.4842 0.7112 1.009 24 640: 100%|██████████| 2/2 [00:00<00:00, 4.33it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.71it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -5094,15 +7077,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 157/200 2.77G 0.5167 0.3632 0.7097 1.067 16 640: 100%|██████████| 2/2 [00:00<00:00, 4.04it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.54it/s]" + " 156/200 2.79G 0.5285 0.4334 0.6367 1.05 30 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.53it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.985 1 0.995 0.907 0.985 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5124,15 +7107,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 158/200 2.77G 0.4502 0.3701 0.6804 0.9987 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.32it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.44it/s]" + " 157/200 2.79G 0.4172 0.4201 0.6559 0.9819 31 640: 100%|██████████| 2/2 [00:00<00:00, 4.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5154,15 +7137,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 159/200 2.77G 0.4283 0.3652 0.6534 1.01 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.48it/s]" + " 158/200 2.79G 0.4992 0.9646 0.725 1.032 33 640: 100%|██████████| 2/2 [00:00<00:00, 3.89it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.81it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.907 0.986 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5184,15 +7167,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 160/200 2.75G 0.6556 0.4277 0.7736 1.161 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.89it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.87it/s]" + " 159/200 2.77G 0.4959 0.4387 0.8432 1.14 18 640: 100%|██████████| 2/2 [00:00<00:00, 4.27it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.21it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.885 0.986 1 0.995 0.951\n" + " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.995\n" ] }, { @@ -5214,15 +7197,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 161/200 2.82G 0.466 0.5268 0.7672 0.9704 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.88it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.71it/s]" + " 160/200 2.77G 0.365 0.3852 0.5683 1.012 29 640: 100%|██████████| 2/2 [00:00<00:00, 4.02it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.40it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.885 0.986 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5244,15 +7227,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 162/200 2.77G 0.6334 0.6523 0.8408 1.081 15 640: 100%|██████████| 2/2 [00:00<00:00, 3.55it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.35it/s]" + " 161/200 2.77G 0.4181 0.4409 0.6087 0.992 28 640: 100%|██████████| 2/2 [00:00<00:00, 3.74it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.03it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5274,15 +7257,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 163/200 2.77G 0.6151 0.4005 0.8177 1.146 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.29it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.02it/s]\n" + " 162/200 2.77G 0.3477 0.431 0.5769 0.9644 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.79it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.87it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.962\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.951\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -5291,15 +7287,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 164/200 2.77G 0.573 0.915 0.8181 1.085 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.27it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.12it/s]" + " 163/200 2.77G 0.3872 0.4061 0.5936 1.015 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.82it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.50it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.962\n" ] }, { @@ -5321,15 +7317,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 165/200 2.75G 0.5735 0.3785 0.8579 1.089 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.83it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 3.74it/s]" + " 164/200 2.79G 0.3674 0.3285 0.554 0.9907 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.49it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.929 0.986 1 0.995 0.962\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.962\n" ] }, { @@ -5351,15 +7347,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 166/200 2.76G 0.4424 0.6388 0.6663 1.006 22 640: 100%|██████████| 2/2 [00:00<00:00, 2.47it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.43it/s]" + " 165/200 2.77G 0.4916 0.4509 0.7087 1.064 29 640: 100%|██████████| 2/2 [00:00<00:00, 5.11it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.97it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.962\n" ] }, { @@ -5381,15 +7377,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 167/200 2.77G 0.4522 0.4865 0.6884 0.9968 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.25it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.86it/s]" + " 166/200 2.79G 0.3455 0.4332 0.6185 0.9756 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.09it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.55it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.986 1 0.995 0.895 0.986 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5411,15 +7407,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 168/200 2.77G 0.5797 0.5913 0.7426 1.044 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.66it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.41it/s]" + " 167/200 2.79G 0.3948 0.4215 0.6113 0.9863 21 640: 100%|██████████| 2/2 [00:00<00:00, 4.65it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5441,15 +7437,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 169/200 2.78G 0.5131 0.453 0.919 1.062 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.28it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.27it/s]" + " 168/200 2.79G 0.4538 0.5063 0.722 1.006 27 640: 100%|██████████| 2/2 [00:00<00:00, 3.93it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.18it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5471,15 +7467,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 170/200 2.75G 0.5183 0.3331 0.6984 1.099 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.51it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.62it/s]" + " 169/200 2.77G 0.5542 0.5127 0.7711 1.14 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.26it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.22it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5501,15 +7497,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 171/200 2.75G 0.4504 0.4429 0.6613 1.007 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.84it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.83it/s]" + " 170/200 2.77G 0.4553 0.4083 0.6467 0.9627 26 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5531,15 +7527,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 172/200 2.75G 0.5363 0.4121 0.8426 1.015 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.42it/s]" + " 171/200 2.76G 0.3799 0.4446 0.5607 0.974 28 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.59it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5561,15 +7557,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 173/200 2.77G 0.6181 0.4741 0.9122 1.09 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.78it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.67it/s]" + " 172/200 2.77G 0.4577 0.5669 0.7086 1.033 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.06it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5591,15 +7587,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 174/200 2.75G 0.4773 0.5172 0.7039 1.005 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.69it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.10it/s]" + " 173/200 2.77G 0.4176 0.3505 0.6044 1.035 30 640: 100%|██████████| 2/2 [00:00<00:00, 3.25it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5621,15 +7617,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 175/200 2.75G 0.6147 0.5295 0.8511 1.167 16 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.72it/s]" + " 174/200 2.77G 0.4283 0.4488 0.6392 1.03 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.80it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.84it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5651,15 +7647,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 176/200 2.75G 0.5011 0.4729 0.6648 1.049 17 640: 100%|██████████| 2/2 [00:00<00:00, 2.37it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.79it/s]\n" + " 175/200 2.77G 0.4155 0.428 0.6205 1.011 26 640: 100%|██████████| 2/2 [00:00<00:00, 2.59it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -5668,15 +7677,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 177/200 2.79G 0.4915 0.4244 0.7123 1.048 21 640: 100%|██████████| 2/2 [00:00<00:00, 2.88it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.67it/s]" + " 176/200 2.77G 0.4463 0.3981 0.6633 1.043 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.60it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.38it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5698,15 +7707,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 178/200 2.77G 0.5064 0.4514 0.6745 1.077 20 640: 100%|██████████| 2/2 [00:00<00:00, 2.21it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.15it/s]" + " 177/200 2.81G 0.4065 0.3935 0.6349 0.99 23 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.25it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5728,15 +7737,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 179/200 2.75G 0.4005 0.3353 0.5836 1.008 21 640: 100%|██████████| 2/2 [00:00<00:00, 3.11it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.12it/s]" + " 178/200 2.82G 0.5287 0.4907 0.6616 1.091 28 640: 100%|██████████| 2/2 [00:00<00:00, 4.02it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.912 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5758,15 +7767,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 180/200 2.75G 0.6025 0.4374 0.7449 1.085 17 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.92it/s]" + " 179/200 2.77G 0.4696 0.4084 0.6529 1.043 25 640: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.13it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -5788,15 +7797,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 181/200 2.77G 0.4942 0.4212 0.6912 1.056 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.81it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.91it/s]" + " 180/200 2.77G 0.4718 0.3939 0.6828 1.069 15 640: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.89it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5818,15 +7827,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 182/200 2.75G 0.5816 0.572 0.8252 1.094 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.56it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.32it/s]" + " 181/200 2.77G 0.4164 0.4149 0.619 1.018 20 640: 100%|██████████| 2/2 [00:00<00:00, 4.35it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5848,15 +7857,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 183/200 2.74G 0.4526 0.5938 0.6959 1.008 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.77it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.01it/s]" + " 182/200 2.79G 0.3885 0.3757 0.6076 0.9955 22 640: 100%|██████████| 2/2 [00:00<00:00, 3.98it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.21it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5878,15 +7887,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 184/200 2.76G 0.4136 0.4118 0.6007 0.9834 16 640: 100%|██████████| 2/2 [00:00<00:00, 3.61it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.57it/s]" + " 183/200 2.77G 0.4219 0.4906 0.6889 1.02 23 640: 100%|██████████| 2/2 [00:00<00:00, 4.16it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.11it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5908,15 +7917,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 185/200 2.75G 0.4407 0.4098 0.7066 1.002 18 640: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.76it/s]" + " 184/200 2.77G 0.4282 0.6303 0.7407 1.015 30 640: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.49it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.951\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5938,15 +7947,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 186/200 2.73G 0.6075 0.6999 0.8587 1.085 19 640: 100%|██████████| 2/2 [00:00<00:00, 3.54it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.49it/s]" + " 185/200 2.77G 0.4778 0.4118 0.7255 1.081 29 640: 100%|██████████| 2/2 [00:00<00:00, 3.50it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5968,15 +7977,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 187/200 2.75G 0.5467 0.4813 0.8547 1.099 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.94it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.77it/s]" + " 186/200 2.77G 0.4588 0.5811 0.6453 1.073 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.96it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 9.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -5998,15 +8007,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 188/200 2.78G 0.4071 0.3879 0.6083 0.9642 19 640: 100%|██████████| 2/2 [00:00<00:00, 2.20it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.28it/s]" + " 187/200 2.77G 0.3905 0.4229 0.6209 1 28 640: 100%|██████████| 2/2 [00:00<00:00, 2.91it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.58it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -6028,15 +8037,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 189/200 2.77G 0.4637 0.4227 0.5746 1.018 15 640: 100%|██████████| 2/2 [00:00<00:00, 2.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.26it/s]\n" + " 188/200 2.79G 0.4073 0.3619 0.5942 0.9655 30 640: 100%|██████████| 2/2 [00:00<00:00, 2.32it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.99it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n", + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6045,15 +8054,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 190/200 2.73G 0.6052 0.622 0.7479 1.1 18 640: 100%|██████████| 2/2 [00:00<00:00, 2.14it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.71it/s]" + " 189/200 2.77G 0.388 0.3389 0.5601 0.9484 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" ] }, { @@ -6067,8 +8076,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Closing dataloader mosaic\n", - "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6077,15 +8084,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 191/200 2.67G 0.2912 0.2252 0.8512 0.9826 7 640: 100%|██████████| 2/2 [00:01<00:00, 1.98it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 3.58it/s]" + " 190/200 2.75G 0.3754 0.4782 0.5723 1.004 23 640: 100%|██████████| 2/2 [00:00<00:00, 2.45it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 15.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6099,6 +8106,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "Closing dataloader mosaic\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6107,15 +8116,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 192/200 2.68G 0.3667 0.3339 0.943 1.032 7 640: 100%|██████████| 2/2 [00:00<00:00, 3.18it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 6.43it/s]" + " 191/200 2.75G 0.3184 0.1912 1.009 1.012 9 640: 100%|██████████| 2/2 [00:00<00:00, 2.08it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 4.45it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6137,15 +8146,32 @@ "name": "stderr", "output_type": "stream", "text": [ - " 193/200 2.67G 0.4169 0.2416 1.138 1.108 6 640: 100%|██████████| 2/2 [00:00<00:00, 4.17it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.34it/s]" + " 192/200 2.7G 0.4175 0.2957 1.168 1.075 9 640: 100%|██████████| 2/2 [00:00<00:00, 3.48it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 7.88it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", + "\n", + " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 193/200 2.75G 0.2685 0.2553 0.8985 0.9084 10 640: 100%|██████████| 2/2 [00:00<00:00, 4.47it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.65it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6167,15 +8193,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 194/200 2.68G 0.34 0.4287 0.8749 0.9711 7 640: 100%|██████████| 2/2 [00:00<00:00, 3.80it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 5.07it/s]" + " 194/200 2.75G 0.4466 0.3397 1.222 1.162 10 640: 100%|██████████| 2/2 [00:00<00:00, 4.33it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.78it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6197,15 +8223,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 195/200 2.67G 0.2565 0.2773 0.7892 0.9078 7 640: 100%|██████████| 2/2 [00:00<00:00, 3.73it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.58it/s]" + " 195/200 2.75G 0.2575 0.2706 0.8466 0.9785 8 640: 100%|██████████| 2/2 [00:00<00:00, 4.58it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.34it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.895 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6227,15 +8253,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 196/200 2.68G 0.357 0.248 1.021 1.044 6 640: 100%|██████████| 2/2 [00:00<00:00, 2.57it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.86it/s]" + " 196/200 2.7G 0.247 0.257 0.7281 0.9132 10 640: 100%|██████████| 2/2 [00:00<00:00, 4.14it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n" + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" ] }, { @@ -6257,15 +8283,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 197/200 2.72G 0.3966 0.2981 1.045 1.056 7 640: 100%|██████████| 2/2 [00:00<00:00, 2.97it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.43it/s]\n" + " 197/200 2.78G 0.3157 0.3094 0.9316 0.9796 10 640: 100%|██████████| 2/2 [00:00<00:00, 3.62it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.929 0.987 1 0.995 0.995\n", + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6274,15 +8300,28 @@ "name": "stderr", "output_type": "stream", "text": [ - " 198/200 2.68G 0.3017 0.3045 0.8399 0.8772 8 640: 100%|██████████| 2/2 [00:00<00:00, 2.44it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.53it/s]\n" + " 198/200 2.7G 0.3052 0.2678 0.8774 1.013 8 640: 100%|██████████| 2/2 [00:00<00:00, 2.73it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.64it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", "\n", " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" ] @@ -6291,8 +8330,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " 199/200 2.67G 0.31 0.2672 0.8157 1.029 8 640: 100%|██████████| 2/2 [00:00<00:00, 2.41it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.49it/s]" + " 199/200 2.75G 0.2492 0.2917 0.7622 0.8656 9 640: 100%|██████████| 2/2 [00:00<00:00, 2.61it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.34it/s]" ] }, { @@ -6321,8 +8360,8 @@ "name": "stderr", "output_type": "stream", "text": [ - " 200/200 2.68G 0.4499 0.263 1.007 1.119 6 640: 100%|██████████| 2/2 [00:00<00:00, 2.72it/s]\n", - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.43it/s]" + " 200/200 2.7G 0.2499 0.3162 0.7796 0.9195 9 640: 100%|██████████| 2/2 [00:00<00:00, 2.68it/s]\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.15it/s]" ] }, { @@ -6344,12 +8383,12 @@ "output_type": "stream", "text": [ "\n", - "200 epochs completed in 0.093 hours.\n", - "Optimizer stripped from runs/segment/train4/weights/last.pt, 6.8MB\n", - "Optimizer stripped from runs/segment/train4/weights/best.pt, 6.8MB\n", + "200 epochs completed in 0.086 hours.\n", + "Optimizer stripped from runs/segment/train/weights/last.pt, 6.8MB\n", + "Optimizer stripped from runs/segment/train/weights/best.pt, 6.8MB\n", "\n", - "Validating runs/segment/train4/weights/best.pt...\n", - "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Validating runs/segment/train/weights/best.pt...\n", + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" ] }, @@ -6357,33 +8396,19 @@ "name": "stderr", "output_type": "stream", "text": [ - " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.38it/s]\n" + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 21.43it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " all 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", - " tractor 3 3 0.987 1 0.995 0.962 0.987 1 0.995 0.995\n", - "Speed: 0.3ms preprocess, 9.0ms inference, 0.0ms loss, 1.7ms postprocess per image\n", - "Results saved to \u001b[1mruns/segment/train4\u001b[0m\n" + " all 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n", + " tractor 3 3 0.987 1 0.995 0.995 0.987 1 0.995 0.995\n", + "Speed: 0.3ms preprocess, 5.2ms inference, 0.0ms loss, 1.5ms postprocess per image\n", + "Results saved to \u001b[1mruns/segment/train\u001b[0m\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "73419dc7b5a942bbbdbfe9e0a2552c23", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.000 MB of 0.000 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -6392,7 +8417,7 @@ " .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n", " .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n", " \n", - "

Run history:


lr/pg0▁▂▂▆▇████▇▇▇▇▇▇▇▆▆▆▆▅▅▅▅▅▄▄▄▄▄▃▃▃▂▂▂▂▁▁▁
lr/pg1▄▄▇▇▇█████▇▇▇▇▆▆▆▅▅▅▄▄▄▄▄▄▄▄▄▄▃▃▃▂▂▂▂▂▁▁
lr/pg2▁▂▂▂▃▅▆▇▇▇███▇▇▇▇▆▆▆▆▅▅▅▅▄▄▄▄▃▃▂▂▂▂▂▂▁▁▁
metrics/mAP50(B)▃▁▂▂▆██████▅████████████████████████████
metrics/mAP50(M)▆▁██████▄███████████████████████████████
metrics/mAP50-95(B)▅▆▁▂▆▇▅▄▅▇▅▅▇▇▆▇▇▇▇▇██▇██▇██▇▇█▇▇▇▇▇▇▇▇█
metrics/mAP50-95(M)▃▆▂▅▆█▁▁▇▇███▇█▇▇▇▇███████████▇▇▇▇███▇██
metrics/precision(B)▁▁▁▁███████▇▇███████████████████████████
metrics/precision(M)▁▁▁▁▁▁▁████████▇████████████████████████
metrics/recall(B)████████▁███████████████████████████████
metrics/recall(M)▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
model/GFLOPs▁
model/parameters▁
model/speed_PyTorch(ms)▁
train/box_loss▇▇▅▆▇▅▆▅▅▂▇█▃▅▄▄▆▆▃▄▆▂▅▃▂▃▄▂▄▃▃▂▁▄▂▁▄▃▂▁
train/cls_loss█▆▅▅▅▄▄▄▄▄▅▅▃▃▄▃▄▄▂▂▃▃▂▂▂▂▂▂▂▂▁▂▁▂▂▁▁▂▂▃
train/dfl_loss█▇▇▇▅▅▅▄▄▆▅▃▃▅▄▄▄▂▃▂▃▄▂▂▃▁▂▃▂▁▁▂▄▃▄▂▁▁▂▄
train/seg_loss█▇▄▄▃▂▂▂▂▃▄▃▂▃▂▂▂▂▂▂▂▂▂▁▂▂▁▁▁▁▁▁▂▁▁▂▂▁▁▁
val/box_loss▁▆▅▅▅▇▆▅▇▂▆▆▇██▄▃▄▄▄▆▄▃▃▂▂▂▂▃▂▂▂▂▂▂▂▂▂▂▂
val/cls_loss███▆▆▆▇▄▄▃▄▄▃▃▄▄▃▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
val/dfl_loss▂▃▄▄▅▆▄▆▃▄▃▄▅▅▇█▇▄▄▄▃▃▃▂▁▂▂▁▁▁▂▂▁▁▁▁▁▁▁▁
val/seg_loss▇█▃▂▂▂▃▂▁▁▁▂▂▁▂▁▁▁▂▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁

Run summary:


lr/pg02e-05
lr/pg12e-05
lr/pg22e-05
metrics/mAP50(B)0.995
metrics/mAP50(M)0.995
metrics/mAP50-95(B)0.96208
metrics/mAP50-95(M)0.995
metrics/precision(B)0.98676
metrics/precision(M)0.98676
metrics/recall(B)1
metrics/recall(M)1
model/GFLOPs12.111
model/parameters3264201
model/speed_PyTorch(ms)141.034
train/box_loss0.44995
train/cls_loss1.00653
train/dfl_loss1.1189
train/seg_loss0.26298
val/box_loss0.39441
val/cls_loss0.50032
val/dfl_loss1.07174
val/seg_loss0.44151

" + "

Run history:


lr/pg0▁▁▁▂▄▄▄▅▅▆▇▇██▇▇▇▇▇▇▆▅▅▅▅▅▅▅▄▄▄▄▃▃▂▂▂▂▂▁
lr/pg1▁▂▃▄▆▇▇▇████▇▇▇▇▇▆▆▆▆▅▅▅▅▅▅▅▄▄▄▃▃▃▂▂▂▂▁▁
lr/pg2▂▃▃▄▄▅▅▆▇▇████▇▇▇▆▆▆▆▆▅▅▅▄▄▄▄▄▃▃▃▃▂▂▂▂▂▁
metrics/mAP50(B)▁███████████████████████████████████████
metrics/mAP50(M)█▆███████▁██████████████████████████████
metrics/mAP50-95(B)▄▃▁▅▅▄▆▂▅▂▇▇▆▆▆▆▆▆▆▇▇▇▇▇█▇▇▇▆▆▇▇▇▇▇▇▇▇▇▇
metrics/mAP50-95(M)▅▆███▇▇▇▇▇▅▃▁▇████▇█████████████████████
metrics/precision(B)▁▁▁▁▁▇██████████████████████████████████
metrics/precision(M)▁▁▁▁▁▁█████████████████▇████████████████
metrics/recall(B)█████████████▁▇█████████████████████████
metrics/recall(M)█████████████▁██████████████████████████
model/GFLOPs▁
model/parameters▁
model/speed_PyTorch(ms)▁
train/box_loss█▅▅▄▄▅▆▄▅▄▄▅▅▅▅▄▆▄▄▃▄▃▅▄▃▃▃▆▄▄▃▄▃▃▂▂▄▃▃▁
train/cls_loss█▄▃▃▃▂▂▃▂▃▂▂▃▃▂▂▃▂▂▂▂▁▂▁▂▁▂▂▁▁▁▁▂▁▁▁▁▁▁▂
train/dfl_loss█▅▅▄▅▄▆▄▄▃▄▄▄▄▄▅▄▆▂▃▃▃▃▃▃▄▃▂▃▂▂▃▃▂▂▂▂▂▂▁
train/seg_loss█▇▆▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▂▂▂▁▃▁▁▁▁▁▁▂▁▁
val/box_loss▂▃▃▃▄▄▄▅▇█▆▄▄▅▅▃▄▄▄▅▄▃▂▃▃▁▁▂▂▂▂▂▃▃▂▁▁▁▁▁
val/cls_loss█▇▇▇▇▅▅▃▄▃▆▂▂▃▃▄▂▃▂▂▂▂▃▂▂▁▂▂▂▁▁▁▁▁▁▁▁▁▁▁
val/dfl_loss▃▃▂▂▂▄▃▂▂▂█▅▄▃▂▂▂▂▄▄▃▂▂▂▂▁▁▁▁▁▂▂▂▂▂▂▁▁▁▁
val/seg_loss▃▄▂▄▄▃▄▅▆█▃▄▄▄▄▃▂▃▃▃▂▂▁▁▂▁▁▁▁▁▁▂▂▂▁▁▁▁▁▁

Run summary:


lr/pg02e-05
lr/pg12e-05
lr/pg22e-05
metrics/mAP50(B)0.995
metrics/mAP50(M)0.995
metrics/mAP50-95(B)0.995
metrics/mAP50-95(M)0.995
metrics/precision(B)0.98703
metrics/precision(M)0.98703
metrics/recall(B)1
metrics/recall(M)1
model/GFLOPs12.111
model/parameters3264201
model/speed_PyTorch(ms)136.898
train/box_loss0.24987
train/cls_loss0.77958
train/dfl_loss0.91948
train/seg_loss0.31617
val/box_loss0.36786
val/cls_loss0.56683
val/dfl_loss1.16621
val/seg_loss0.32534

" ], "text/plain": [ "" @@ -6404,7 +8429,7 @@ { "data": { "text/html": [ - "You can sync this run to the cloud by running:
wandb sync /content/wandb/offline-run-20241021_000800-nado7ubu" + "You can sync this run to the cloud by running:
wandb sync /content/wandb/offline-run-20241024_203139-ozs9fgrx" ], "text/plain": [ "" @@ -6416,7 +8441,7 @@ { "data": { "text/html": [ - "Find logs at: ./wandb/offline-run-20241021_000800-nado7ubu/logs" + "Find logs at: ./wandb/offline-run-20241024_203139-ozs9fgrx/logs" ], "text/plain": [ "" @@ -6453,12 +8478,12 @@ "height": 1000 }, "id": "b559b1f9", - "outputId": "b2389b7d-a09c-4099-ba0d-6c722e267a06" + "outputId": "bcb3fae6-27eb-4384-f532-c573ae45c599" }, "outputs": [ { "data": { - "image/jpeg": "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", + "image/jpeg": "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", "text/plain": [ "" ] @@ -6481,14 +8506,14 @@ "base_uri": "https://localhost:8080/" }, "id": "dec0cb11", - "outputId": "fca326c9-c7e1-4767-8e07-383c971a89eb" + "outputId": "72cf4330-fa0f-47aa-82c5-242dc6978dcd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" ] }, @@ -6497,17 +8522,17 @@ "output_type": "stream", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00