diff --git a/notebooks/train-yolov5-object-detection-on-custom-data.ipynb b/notebooks/train-yolov5-object-detection-on-custom-data.ipynb
index 7681142..9ba952d 100644
--- a/notebooks/train-yolov5-object-detection-on-custom-data.ipynb
+++ b/notebooks/train-yolov5-object-detection-on-custom-data.ipynb
@@ -6,8 +6,12 @@
"id": "GD9gUQpaBxNa"
},
"source": [
+ "[](https://github.com/roboflow/notebooks)\n",
+ "\n",
"# How to Train YOLOv5 on Custom Objects\n",
"\n",
+ "**⚠️ This notebook was updated on October 24th, 2023. Since then, the way to load data into the notebook has changed. Thus, this notebook is not the same as the original version used in the accompanying YouTube video or blog post.**\n",
+ "\n",
"This tutorial is based on the [YOLOv5 repository](https://github.com/ultralytics/yolov5) by [Ultralytics](https://www.ultralytics.com/). This notebook shows training on **your own custom objects**. Many thanks to Ultralytics for putting this repository together - we hope that in combination with clean data management tools at Roboflow, this technologoy will become easily accessible to any developer wishing to use computer vision in their projects.\n",
"\n",
"### Accompanying Blog Post\n",
@@ -29,16 +33,7 @@
"* Run YOLOv5 inference on test images\n",
"* Export saved YOLOv5 weights for future inference\n",
"\n",
- "\n",
- "\n",
- "### **About**\n",
- "\n",
- "[Roboflow](https://roboflow.com) enables teams to deploy custom computer vision models quickly and accurately. Convert data from to annotation format, assess dataset health, preprocess, augment, and more. It's free for your first 1000 source images.\n",
- "\n",
- "**Looking for a vision model available via API without hassle? Try Roboflow Train.**\n",
- "\n",
- "\n",
- "\n"
+ "Let's get started!"
]
},
{
@@ -54,31 +49,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ie5uLDH4uzAp",
- "outputId": "d2e38392-e09e-4caf-e309-72b5354a5c9b",
+ "outputId": "5aaf0b1c-4892-46ea-9273-9a22994dfb20",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"Cloning into 'yolov5'...\n",
- "remote: Enumerating objects: 12811, done.\u001b[K\n",
- "remote: Counting objects: 100% (92/92), done.\u001b[K\n",
- "remote: Compressing objects: 100% (48/48), done.\u001b[K\n",
- "remote: Total 12811 (delta 52), reused 75 (delta 44), pack-reused 12719\u001b[K\n",
- "Receiving objects: 100% (12811/12811), 12.40 MiB | 21.02 MiB/s, done.\n",
- "Resolving deltas: 100% (8807/8807), done.\n",
+ "remote: Enumerating objects: 16013, done.\u001b[K\n",
+ "remote: Counting objects: 100% (46/46), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
+ "remote: Total 16013 (delta 25), reused 23 (delta 13), pack-reused 15967\u001b[K\n",
+ "Receiving objects: 100% (16013/16013), 14.68 MiB | 17.18 MiB/s, done.\n",
+ "Resolving deltas: 100% (10991/10991), done.\n",
"/content/yolov5\n",
- "HEAD is now at fbe67e4 Fix `OMP_NUM_THREADS=1` for macOS (#8624)\n"
+ "HEAD is now at 064365d Update parse_opt() in export.py to work as in train.py (#10789)\n"
]
}
],
@@ -91,24 +86,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wbvMlHd_QwMG",
- "outputId": "484dd5f5-a33e-4e56-ac20-10dc71b36009",
+ "outputId": "04bf61dc-0730-422e-cf30-481ece54b934",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
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- "\u001b[?25hSetup complete. Using torch 1.12.0+cu113 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', major=6, minor=0, total_memory=16280MB, multi_processor_count=56)\n"
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.6/190.6 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hSetup complete. Using torch 2.1.0+cu118 _CudaDeviceProperties(name='Tesla T4', major=7, minor=5, total_memory=15101MB, multi_processor_count=40)\n"
]
}
],
@@ -130,11 +127,73 @@
"id": "SDIhrBF0sPaM"
},
"source": [
- "# Download Correctly Formatted Custom Dataset \n",
+ "## Roboflow Universe\n",
+ "\n",
+ "Need data for your project? Before spending time on annotating, check out Roboflow Universe, a repository of more than 200,000 open-source datasets that you can use in your projects. You'll find datasets containing everything from annotated cracks in concrete to plant images with disease annotations.\n",
+ "\n",
+ "\n",
+ "[](https://universe.roboflow.com/)\n",
+ "\n",
+ "## Preparing a custom dataset\n",
+ "\n",
+ "Building a custom dataset can be a painful process. It might take dozens or even hundreds of hours to collect images, label them, and export them in the proper format. Fortunately, Roboflow makes this process as straightforward and fast as possible. Let me show you how!\n",
+ "\n",
+ "### Step 1: Creating project\n",
+ "\n",
+ "Before you start, you need to create a Roboflow [account](https://app.roboflow.com/login). Once you do that, you can create a new project in the Roboflow [dashboard](https://app.roboflow.com/). Keep in mind to choose the right project type. In our case, Object Detection.\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Step 2: Uploading images\n",
+ "\n",
+ "Next, add the data to your newly created project. You can do it via API or through our [web interface](https://docs.roboflow.com/adding-data/object-detection).\n",
+ "\n",
+ "If you drag and drop a directory with a dataset in a supported format, the Roboflow dashboard will automatically read the images and annotations together.\n",
"\n",
- "We'll download our dataset from Roboflow. Use the \"**YOLOv5 PyTorch**\" export format. Note that the Ultralytics implementation calls for a YAML file defining where your training and test data is. The Roboflow export also writes this format for us.\n",
+ "\n",
"\n",
- "To get your data into Roboflow, follow the [Getting Started Guide](https://blog.roboflow.ai/getting-started-with-roboflow/)."
+ "### Step 3: Labeling\n",
+ "\n",
+ "If you only have images, you can label them in [Roboflow Annotate](https://docs.roboflow.com/annotate).\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Step 4: Generate new dataset version\n",
+ "\n",
+ "Now that we have our images and annotations added, we can Generate a Dataset Version. When Generating a Version, you may elect to add preprocessing and augmentations. This step is completely optional, however, it can allow you to significantly improve the robustness of your model.\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Step 5: Exporting dataset\n",
+ "\n",
+ "Once the dataset version is generated, we have a hosted dataset we can load directly into our notebook for easy training. Click `Export` and select the `YOLO v5 PyTorch` dataset format.\n",
+ "\n",
+ "\n"
]
},
{
@@ -143,119 +202,131 @@
"id": "vDeebwqS9JbZ"
},
"source": [
+ "## Step 6: Download a Dataset\n",
"\n",
- "\n",
- "\n"
+ "Add your Roboflow API key below to download the default money counting dataset. Alternatively, use the code provided by the Roboflow dashboard in the above step to load a custom dataset."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Knxi2ncxWffW",
- "outputId": "d4d0155d-63c9-4c2c-a63e-a553ecbef611",
+ "outputId": "067d051c-9ae6-4cf0-db2e-fac32b2df01a",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
+ "output_type": "stream",
"name": "stdout",
+ "text": [
+ "loading Roboflow workspace...\n",
+ "loading Roboflow project...\n"
+ ]
+ },
+ {
"output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Downloading Dataset Version Zip in Cash-Counter-10 to yolov5pytorch:: 100%|██████████| 121603/121603 [00:02<00:00, 59447.22it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
"text": [
- "\u001b[K |████████████████████████████████| 178 kB 7.6 MB/s \n",
- "\u001b[K |████████████████████████████████| 21.6 MB 1.2 MB/s \n",
- "\u001b[K |████████████████████████████████| 54 kB 2.8 MB/s \n",
- "\u001b[K |████████████████████████████████| 67 kB 6.4 MB/s \n",
- "\u001b[K |████████████████████████████████| 1.1 MB 68.0 MB/s \n",
- "\u001b[K |████████████████████████████████| 145 kB 72.2 MB/s \n",
- "\u001b[K |████████████████████████████████| 4.3 MB 55.6 MB/s \n",
- "\u001b[K |████████████████████████████████| 138 kB 68.1 MB/s \n",
- "\u001b[K |████████████████████████████████| 62 kB 1.7 MB/s \n",
- "\u001b[?25h Building wheel for wget (setup.py) ... \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-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.\n",
- "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
- "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
- "upload and label your dataset, and get an API KEY here: https://app.roboflow.com/?model=yolov5&ref=roboflow-yolov5\n"
+ "\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n",
+ "Extracting Dataset Version Zip to Cash-Counter-10 in yolov5pytorch:: 100%|██████████| 6112/6112 [00:00<00:00, 7450.91it/s]\n"
]
}
],
"source": [
- "#follow the link below to get your download code from from Roboflow\n",
"!pip install -q roboflow\n",
+ "\n",
"from roboflow import Roboflow\n",
- "rf = Roboflow(model_format=\"yolov5\", notebook=\"roboflow-yolov5\")"
+ "\n",
+ "rf = Roboflow(api_key=\"YOUR_API_KEY\")\n",
+ "project = rf.workspace(\"alex-hyams-cosqx\").project(\"cash-counter\")\n",
+ "dataset = project.version(10).download(\"yolov5\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ug_PhK1oqwQA",
- "outputId": "b417162b-c116-416c-88e8-96b2563d1ee1",
+ "outputId": "3807a1b7-0e6b-4c1b-c1bc-f2d32d626811",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/content/yolov5\n",
- "loading Roboflow workspace...\n",
- "loading Roboflow project...\n",
- "Downloading Dataset Version Zip in American-Mushrooms-1 to yolov5pytorch: 100% [3866359 / 3866359] bytes\n"
- ]
- },
- {
- "name": "stderr",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting Dataset Version Zip to American-Mushrooms-1 in yolov5pytorch:: 100%|██████████| 278/278 [00:00<00:00, 1111.85it/s]\n"
+ "/content/yolov5\n"
]
}
],
"source": [
- "%cd /content/yolov5\n",
- "#after following the link above, recieve python code with these fields filled in\n",
- "#from roboflow import Roboflow\n",
- "#rf = Roboflow(api_key=\"YOUR API KEY HERE\")\n",
- "#project = rf.workspace().project(\"YOUR PROJECT\")\n",
- "#dataset = project.version(\"YOUR VERSION\").download(\"yolov5\")"
+ "%cd /content/yolov5"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZZ3DmmGQztJj",
- "outputId": "895f8313-8441-4c2d-9c87-01cc89f22acb",
+ "outputId": "b710c5de-9fd6-4ca7-8053-de3b5bdae0c6",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"names:\n",
- "- CoW\n",
- "- chanterelle\n",
- "nc: 2\n",
- "train: American-Mushrooms-1/train/images\n",
- "val: American-Mushrooms-1/valid/images\n"
+ "- Dime\n",
+ "- Nickel\n",
+ "- Penny\n",
+ "- Quarter\n",
+ "- fifty\n",
+ "- five\n",
+ "- hundred\n",
+ "- one\n",
+ "- ten\n",
+ "- twenty\n",
+ "nc: 10\n",
+ "roboflow:\n",
+ " license: CC BY 4.0\n",
+ " project: cash-counter\n",
+ " url: https://universe.roboflow.com/alex-hyams-cosqx/cash-counter/dataset/10\n",
+ " version: 10\n",
+ " workspace: alex-hyams-cosqx\n",
+ "test: ../test/images\n",
+ "train: Cash-Counter-10/train/images\n",
+ "val: Cash-Counter-10/valid/images\n"
]
}
],
@@ -279,7 +350,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {
"id": "dOPn9wjOAwwK",
"vscode": {
@@ -296,49 +367,49 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1Rvt5wilnDyX",
- "outputId": "8b8386bc-0e05-49a8-c1e0-48a836a0b399",
+ "outputId": "717ac830-0313-4132-cd38-7bbebec1d695",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "# parameters\n",
+ "# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n",
+ "\n",
+ "# Parameters\n",
"nc: 80 # number of classes\n",
"depth_multiple: 0.33 # model depth multiple\n",
"width_multiple: 0.50 # layer channel multiple\n",
- "\n",
- "# anchors\n",
"anchors:\n",
" - [10,13, 16,30, 33,23] # P3/8\n",
" - [30,61, 62,45, 59,119] # P4/16\n",
" - [116,90, 156,198, 373,326] # P5/32\n",
"\n",
- "# YOLOv5 backbone\n",
+ "# YOLOv5 v6.0 backbone\n",
"backbone:\n",
" # [from, number, module, args]\n",
- " [[-1, 1, Focus, [64, 3]], # 0-P1/2\n",
+ " [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2\n",
" [-1, 1, Conv, [128, 3, 2]], # 1-P2/4\n",
" [-1, 3, C3, [128]],\n",
" [-1, 1, Conv, [256, 3, 2]], # 3-P3/8\n",
- " [-1, 9, C3, [256]],\n",
+ " [-1, 6, C3, [256]],\n",
" [-1, 1, Conv, [512, 3, 2]], # 5-P4/16\n",
" [-1, 9, C3, [512]],\n",
" [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32\n",
- " [-1, 1, SPP, [1024, [5, 9, 13]]],\n",
- " [-1, 3, C3, [1024, False]], # 9\n",
+ " [-1, 3, C3, [1024]],\n",
+ " [-1, 1, SPPF, [1024, 5]], # 9\n",
" ]\n",
"\n",
- "# YOLOv5 head\n",
+ "# YOLOv5 v6.0 head\n",
"head:\n",
" [[-1, 1, Conv, [512, 1, 1]],\n",
" [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n",
@@ -364,13 +435,13 @@
}
],
"source": [
- "#this is the model configuration we will use for our tutorial \n",
+ "#this is the model configuration we will use for our tutorial\n",
"%cat /content/yolov5/models/yolov5s.yaml"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"id": "t14hhyqdmw6O",
"vscode": {
@@ -390,7 +461,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {
"id": "uDxebz13RdRA",
"vscode": {
@@ -476,39 +547,48 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1NcFxRcFdJ_O",
- "outputId": "84614320-0d9c-40c2-dbef-a2c121ab667b",
+ "outputId": "5d81137a-2d31-42a7-e0a4-b0675337ef96",
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"/content/yolov5\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ WARNING: code is out of date by 577 commits. Use 'git pull' to update or 'git clone https://github.com/ultralytics/yolov5' to download latest.\n",
- "YOLOv5 v4.0-126-g886f1c0 torch 1.9.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)\n",
+ "2023-10-24 10:18:27.283913: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2023-10-24 10:18:27.283968: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2023-10-24 10:18:27.284008: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2023-10-24 10:18:27.291751: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2023-10-24 10:18:28.448223: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+ "\u001b[34m\u001b[1mtrain: \u001b[0mweights=, cfg=./models/custom_yolov5s.yaml, data=/content/yolov5/Cash-Counter-10/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=416, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=yolov5s_results, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
+ "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 156 commits. Use `git pull` or `git clone https://github.com/ultralytics/yolov5` to update.\n",
+ "YOLOv5 🚀 v7.0-72-g064365d Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
- "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='./models/custom_yolov5s.yaml', data='/content/yolov5/American-Mushrooms-1/data.yaml', device='', entity=None, epochs=100, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[416, 416], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='yolov5s_results', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/yolov5s_results', single_cls=False, sync_bn=False, total_batch_size=16, weights='', workers=8, world_size=1)\n",
- "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
- "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
- "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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, mosaic=1.0, mixup=0.0\n",
+ "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
+ "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
+ "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
+ "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
+ "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
+ "100% 755k/755k [00:00<00:00, 19.6MB/s]\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Focus [3, 32, 3] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
- " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n",
+ " 4 -1 3 161152 models.common.BottleneckCSP [128, 128, 3] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
- " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n",
+ " 6 -1 3 641792 models.common.BottleneckCSP [256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n",
" 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
@@ -526,48 +606,548 @@
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
- " 24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
- "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n",
- " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
- "Model Summary: 283 layers, 7257791 parameters, 7257791 gradients, 16.8 GFLOPS\n",
- "\n",
- "Scaled weight_decay = 0.0005\n",
- "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'American-Mushrooms-1/train/labels' for images and labels... 123 found, 0 missing, 0 empty, 0 corrupted: 100% 123/123 [00:00<00:00, 2407.13it/s]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: American-Mushrooms-1/train/labels.cache\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 123/123 [00:00<00:00, 511.60it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning 'American-Mushrooms-1/valid/labels' for images and labels... 5 found, 0 missing, 0 empty, 0 corrupted: 100% 5/5 [00:00<00:00, 1242.98it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: American-Mushrooms-1/valid/labels.cache\n",
- "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB): 100% 5/5 [00:00<00:00, 321.12it/s]\n",
- "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
- "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
- "Plotting labels... \n",
- "\n",
- "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.48, Best Possible Recall (BPR) = 1.0000\n",
- "Image sizes 416 train, 416 test\n",
+ " 24 [17, 20, 23] 1 40455 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
+ "custom_YOLOv5s summary: 233 layers, 7279367 parameters, 7279367 gradients\n",
+ "\n",
+ "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
+ "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 59 weight(decay=0.0), 70 weight(decay=0.0005), 62 bias\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",
+ "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov5/Cash-Counter-10/train/labels... 2670 images, 914 backgrounds, 0 corrupt: 100% 2670/2670 [00:01<00:00, 1924.04it/s]\n",
+ "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov5/Cash-Counter-10/train/labels.cache\n",
+ "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (1.3GB ram): 100% 2670/2670 [00:14<00:00, 188.00it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov5/Cash-Counter-10/valid/labels... 249 images, 83 backgrounds, 0 corrupt: 100% 249/249 [00:00<00:00, 634.84it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolov5/Cash-Counter-10/valid/labels.cache\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 249/249 [00:02<00:00, 92.86it/s]\n",
+ "\n",
+ "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.98 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
+ "Plotting labels to runs/train/yolov5s_results/labels.jpg... \n",
+ "Image sizes 416 train, 416 val\n",
"Using 2 dataloader workers\n",
- "Logging results to runs/train/yolov5s_results\n",
+ "Logging results to \u001b[1mruns/train/yolov5s_results\u001b[0m\n",
"Starting training for 100 epochs...\n",
"\n",
- " Epoch gpu_mem box obj cls total targets img_size\n",
- " 0/99 1.41G 0.1144 0.02597 0.02773 0.1681 49 416: 100% 8/8 [00:03<00:00, 2.55it/s]\n",
- " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 4.55it/s]\n",
- " all 5 14 0.00492 0.478 0.00691 0.00154\n",
- "\n",
- " Epoch gpu_mem box obj cls total targets img_size\n",
- " 0% 0/8 [00:00\n",
- " train(hyp, opt, device, tb_writer, wandb)\n",
- " File \"train.py\", line 304, in train\n",
- " scaler.scale(loss).backward()\n",
- " File \"/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\", line 255, in backward\n",
- " torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)\n",
- " File \"/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\", line 149, in backward\n",
- " allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag\n",
- "KeyboardInterrupt\n",
- "CPU times: user 173 ms, sys: 37.6 ms, total: 211 ms\n",
- "Wall time: 25.7 s\n"
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 0/99 1.68G 0.09775 0.04436 0.0621 42 416: 100% 167/167 [00:35<00:00, 4.72it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:03<00:00, 2.17it/s]\n",
+ " all 249 1379 0.000415 0.0169 0.000222 3.82e-05\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 1/99 1.84G 0.09002 0.04745 0.05694 77 416: 100% 167/167 [00:28<00:00, 5.89it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.94it/s]\n",
+ " all 249 1379 0.00403 0.0824 0.00376 0.000841\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 2/99 1.84G 0.08354 0.04398 0.05172 69 416: 100% 167/167 [00:27<00:00, 6.07it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 5.14it/s]\n",
+ " all 249 1379 0.419 0.0175 0.00635 0.00225\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 3/99 1.84G 0.07113 0.04331 0.04881 21 416: 100% 167/167 [00:27<00:00, 6.11it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:06<00:00, 1.29it/s]\n",
+ " all 249 1379 0.673 0.189 0.0875 0.0319\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 4/99 1.84G 0.05779 0.03983 0.04666 87 416: 100% 167/167 [00:27<00:00, 6.02it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.08it/s]\n",
+ " all 249 1379 0.706 0.311 0.146 0.0664\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 5/99 1.84G 0.05051 0.03817 0.04587 43 416: 100% 167/167 [00:27<00:00, 6.11it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.99it/s]\n",
+ " all 249 1379 0.721 0.324 0.195 0.0886\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 6/99 1.84G 0.0459 0.03622 0.04517 107 416: 100% 167/167 [00:27<00:00, 6.11it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.13it/s]\n",
+ " all 249 1379 0.725 0.342 0.231 0.14\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 7/99 1.84G 0.0433 0.03545 0.0443 45 416: 100% 167/167 [00:27<00:00, 6.14it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.36it/s]\n",
+ " all 249 1379 0.504 0.392 0.312 0.147\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 8/99 1.84G 0.04119 0.03564 0.04325 115 416: 100% 167/167 [00:28<00:00, 5.80it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.46it/s]\n",
+ " all 249 1379 0.281 0.435 0.287 0.151\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 9/99 1.84G 0.03922 0.03452 0.04243 68 416: 100% 167/167 [00:27<00:00, 6.04it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.16it/s]\n",
+ " all 249 1379 0.799 0.272 0.328 0.205\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 10/99 1.84G 0.03885 0.0336 0.04172 31 416: 100% 167/167 [00:27<00:00, 6.07it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.43it/s]\n",
+ " all 249 1379 0.386 0.393 0.343 0.22\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 11/99 1.84G 0.03671 0.03205 0.03982 98 416: 100% 167/167 [00:27<00:00, 6.13it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.52it/s]\n",
+ " all 249 1379 0.428 0.444 0.383 0.245\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 12/99 1.84G 0.03713 0.03193 0.03942 88 416: 100% 167/167 [00:27<00:00, 6.10it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.45it/s]\n",
+ " all 249 1379 0.343 0.508 0.389 0.251\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 13/99 1.84G 0.03644 0.03261 0.0386 72 416: 100% 167/167 [00:27<00:00, 6.12it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.59it/s]\n",
+ " all 249 1379 0.37 0.49 0.404 0.194\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 14/99 1.84G 0.03624 0.03257 0.03792 82 416: 100% 167/167 [00:27<00:00, 6.08it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.52it/s]\n",
+ " all 249 1379 0.301 0.643 0.42 0.272\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 15/99 1.84G 0.03456 0.03264 0.03665 144 416: 100% 167/167 [00:27<00:00, 6.13it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.07it/s]\n",
+ " all 249 1379 0.251 0.549 0.416 0.257\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 16/99 1.84G 0.03397 0.03066 0.03658 81 416: 100% 167/167 [00:27<00:00, 6.14it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.12it/s]\n",
+ " all 249 1379 0.251 0.633 0.422 0.245\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 17/99 1.84G 0.03464 0.03155 0.03478 71 416: 100% 167/167 [00:27<00:00, 6.07it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.59it/s]\n",
+ " all 249 1379 0.274 0.528 0.415 0.253\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 18/99 1.84G 0.03406 0.03187 0.03437 8 416: 100% 167/167 [00:27<00:00, 6.18it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.63it/s]\n",
+ " all 249 1379 0.3 0.555 0.427 0.239\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 19/99 1.84G 0.03348 0.03082 0.03527 102 416: 100% 167/167 [00:27<00:00, 6.17it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.42it/s]\n",
+ " all 249 1379 0.292 0.667 0.413 0.278\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 20/99 1.84G 0.03336 0.03093 0.03424 33 416: 100% 167/167 [00:27<00:00, 6.10it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.61it/s]\n",
+ " all 249 1379 0.362 0.583 0.437 0.282\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 21/99 1.84G 0.03338 0.03025 0.03368 80 416: 100% 167/167 [00:26<00:00, 6.33it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.44it/s]\n",
+ " all 249 1379 0.278 0.657 0.435 0.268\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 22/99 1.84G 0.03199 0.03174 0.03265 25 416: 100% 167/167 [00:27<00:00, 6.13it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.66it/s]\n",
+ " all 249 1379 0.276 0.577 0.46 0.306\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 23/99 1.84G 0.03277 0.02992 0.03366 36 416: 100% 167/167 [00:27<00:00, 6.16it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.73it/s]\n",
+ " all 249 1379 0.288 0.618 0.476 0.32\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 24/99 1.84G 0.03226 0.029 0.03289 57 416: 100% 167/167 [00:26<00:00, 6.23it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.64it/s]\n",
+ " all 249 1379 0.282 0.539 0.443 0.287\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 25/99 1.84G 0.03243 0.03037 0.03249 48 416: 100% 167/167 [00:28<00:00, 5.79it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.77it/s]\n",
+ " all 249 1379 0.333 0.543 0.47 0.303\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 26/99 1.84G 0.03206 0.0289 0.03225 27 416: 100% 167/167 [00:26<00:00, 6.22it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.91it/s]\n",
+ " all 249 1379 0.385 0.685 0.455 0.308\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 27/99 1.84G 0.03216 0.02981 0.03224 105 416: 100% 167/167 [00:26<00:00, 6.22it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.14it/s]\n",
+ " all 249 1379 0.327 0.635 0.458 0.285\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 28/99 1.84G 0.03141 0.03002 0.03074 80 416: 100% 167/167 [00:27<00:00, 6.15it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.37it/s]\n",
+ " all 249 1379 0.386 0.695 0.477 0.308\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 29/99 1.84G 0.03103 0.03057 0.03039 39 416: 100% 167/167 [00:26<00:00, 6.23it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.51it/s]\n",
+ " all 249 1379 0.331 0.68 0.483 0.33\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 30/99 1.84G 0.03102 0.02814 0.03114 53 416: 100% 167/167 [00:27<00:00, 6.17it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.77it/s]\n",
+ " all 249 1379 0.319 0.639 0.452 0.305\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 31/99 1.84G 0.0314 0.02844 0.03106 98 416: 100% 167/167 [00:27<00:00, 6.18it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.68it/s]\n",
+ " all 249 1379 0.342 0.728 0.489 0.293\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 32/99 1.84G 0.03055 0.02902 0.02847 59 416: 100% 167/167 [00:26<00:00, 6.20it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.84it/s]\n",
+ " all 249 1379 0.354 0.758 0.514 0.334\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 33/99 1.84G 0.03032 0.02914 0.0285 121 416: 100% 167/167 [00:28<00:00, 5.79it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.90it/s]\n",
+ " all 249 1379 0.338 0.795 0.518 0.354\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 34/99 1.84G 0.02985 0.02742 0.03082 102 416: 100% 167/167 [00:27<00:00, 6.17it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.51it/s]\n",
+ " all 249 1379 0.354 0.753 0.52 0.353\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 35/99 1.84G 0.03064 0.02948 0.02885 85 416: 100% 167/167 [00:27<00:00, 6.03it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.17it/s]\n",
+ " all 249 1379 0.362 0.688 0.517 0.336\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 36/99 1.84G 0.03074 0.02933 0.02895 108 416: 100% 167/167 [00:27<00:00, 6.02it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.83it/s]\n",
+ " all 249 1379 0.389 0.714 0.53 0.351\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 37/99 1.84G 0.02974 0.02851 0.0272 78 416: 100% 167/167 [00:26<00:00, 6.20it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.83it/s]\n",
+ " all 249 1379 0.351 0.709 0.495 0.336\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 38/99 1.84G 0.0293 0.02952 0.02935 235 416: 100% 167/167 [00:26<00:00, 6.23it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.91it/s]\n",
+ " all 249 1379 0.358 0.767 0.538 0.353\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 39/99 1.84G 0.02917 0.0272 0.02802 88 416: 100% 167/167 [00:26<00:00, 6.26it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.99it/s]\n",
+ " all 249 1379 0.359 0.797 0.525 0.349\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 40/99 1.84G 0.02958 0.02936 0.02784 60 416: 100% 167/167 [00:27<00:00, 6.16it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 5.01it/s]\n",
+ " all 249 1379 0.374 0.799 0.529 0.335\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 41/99 1.84G 0.02882 0.0281 0.02712 149 416: 100% 167/167 [00:26<00:00, 6.30it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.86it/s]\n",
+ " all 249 1379 0.412 0.678 0.562 0.373\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 42/99 1.84G 0.03004 0.02807 0.02852 76 416: 100% 167/167 [00:27<00:00, 5.97it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.99it/s]\n",
+ " all 249 1379 0.41 0.721 0.556 0.378\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 43/99 1.84G 0.0294 0.02797 0.02815 61 416: 100% 167/167 [00:26<00:00, 6.27it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.50it/s]\n",
+ " all 249 1379 0.425 0.751 0.553 0.382\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 44/99 1.84G 0.02861 0.0278 0.02785 22 416: 100% 167/167 [00:26<00:00, 6.24it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.37it/s]\n",
+ " all 249 1379 0.402 0.763 0.525 0.346\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 45/99 1.84G 0.02854 0.02819 0.02707 45 416: 100% 167/167 [00:26<00:00, 6.31it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.39it/s]\n",
+ " all 249 1379 0.381 0.728 0.573 0.397\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 46/99 1.84G 0.0286 0.02739 0.02689 124 416: 100% 167/167 [00:26<00:00, 6.27it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.80it/s]\n",
+ " all 249 1379 0.354 0.674 0.53 0.358\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 47/99 1.84G 0.02892 0.0284 0.02604 77 416: 100% 167/167 [00:26<00:00, 6.22it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.90it/s]\n",
+ " all 249 1379 0.405 0.676 0.552 0.373\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 48/99 1.84G 0.02906 0.02854 0.02638 60 416: 100% 167/167 [00:26<00:00, 6.28it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.55it/s]\n",
+ " all 249 1379 0.388 0.7 0.568 0.399\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 49/99 1.84G 0.02873 0.02854 0.02655 53 416: 100% 167/167 [00:26<00:00, 6.28it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.87it/s]\n",
+ " all 249 1379 0.41 0.682 0.582 0.4\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 50/99 1.84G 0.02836 0.02612 0.0271 65 416: 100% 167/167 [00:28<00:00, 5.88it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.77it/s]\n",
+ " all 249 1379 0.411 0.791 0.583 0.399\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 51/99 1.84G 0.02817 0.02724 0.0258 87 416: 100% 167/167 [00:26<00:00, 6.31it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.89it/s]\n",
+ " all 249 1379 0.384 0.677 0.576 0.394\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 52/99 1.84G 0.02736 0.02737 0.02469 46 416: 100% 167/167 [00:26<00:00, 6.28it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.49it/s]\n",
+ " all 249 1379 0.39 0.723 0.561 0.383\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 53/99 1.84G 0.02792 0.02821 0.02469 58 416: 100% 167/167 [00:26<00:00, 6.28it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.52it/s]\n",
+ " all 249 1379 0.41 0.793 0.575 0.398\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 54/99 1.84G 0.02776 0.02762 0.02611 85 416: 100% 167/167 [00:26<00:00, 6.34it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.18it/s]\n",
+ " all 249 1379 0.453 0.682 0.566 0.394\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 55/99 1.84G 0.02811 0.02673 0.02686 63 416: 100% 167/167 [00:26<00:00, 6.29it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.73it/s]\n",
+ " all 249 1379 0.407 0.744 0.567 0.384\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 56/99 1.84G 0.0278 0.02727 0.02552 51 416: 100% 167/167 [00:26<00:00, 6.25it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.63it/s]\n",
+ " all 249 1379 0.399 0.707 0.578 0.406\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 57/99 1.84G 0.02775 0.02714 0.02418 103 416: 100% 167/167 [00:27<00:00, 6.10it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.92it/s]\n",
+ " all 249 1379 0.407 0.848 0.593 0.399\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 58/99 1.84G 0.02815 0.02696 0.0247 82 416: 100% 167/167 [00:27<00:00, 6.14it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.41it/s]\n",
+ " all 249 1379 0.42 0.819 0.591 0.398\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 59/99 1.84G 0.02718 0.02709 0.02424 91 416: 100% 167/167 [00:26<00:00, 6.27it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.99it/s]\n",
+ " all 249 1379 0.441 0.761 0.601 0.422\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 60/99 1.84G 0.02701 0.02622 0.02344 38 416: 100% 167/167 [00:26<00:00, 6.28it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.16it/s]\n",
+ " all 249 1379 0.436 0.857 0.607 0.414\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 61/99 1.84G 0.02717 0.02683 0.02334 66 416: 100% 167/167 [00:26<00:00, 6.31it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.53it/s]\n",
+ " all 249 1379 0.423 0.756 0.586 0.399\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 62/99 1.84G 0.02721 0.02661 0.02401 57 416: 100% 167/167 [00:26<00:00, 6.31it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.15it/s]\n",
+ " all 249 1379 0.439 0.737 0.589 0.394\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 63/99 1.84G 0.02742 0.02583 0.02359 98 416: 100% 167/167 [00:27<00:00, 6.02it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.61it/s]\n",
+ " all 249 1379 0.404 0.732 0.578 0.39\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 64/99 1.84G 0.02714 0.02731 0.02258 45 416: 100% 167/167 [00:28<00:00, 5.82it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.65it/s]\n",
+ " all 249 1379 0.456 0.731 0.594 0.407\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 65/99 1.84G 0.0266 0.02584 0.02354 39 416: 100% 167/167 [00:27<00:00, 5.99it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.79it/s]\n",
+ " all 249 1379 0.45 0.853 0.591 0.41\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 66/99 1.84G 0.02738 0.02611 0.0235 53 416: 100% 167/167 [00:27<00:00, 6.00it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.36it/s]\n",
+ " all 249 1379 0.451 0.797 0.582 0.414\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 67/99 1.84G 0.02695 0.02622 0.02324 48 416: 100% 167/167 [00:30<00:00, 5.50it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.30it/s]\n",
+ " all 249 1379 0.452 0.837 0.589 0.417\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 68/99 1.84G 0.02664 0.02507 0.0239 150 416: 100% 167/167 [00:27<00:00, 6.08it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.76it/s]\n",
+ " all 249 1379 0.455 0.852 0.565 0.398\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 69/99 1.84G 0.02612 0.02559 0.02371 86 416: 100% 167/167 [00:28<00:00, 5.95it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.84it/s]\n",
+ " all 249 1379 0.464 0.841 0.589 0.408\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 70/99 1.84G 0.02637 0.02583 0.02237 52 416: 100% 167/167 [00:27<00:00, 6.15it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.80it/s]\n",
+ " all 249 1379 0.485 0.871 0.594 0.422\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 71/99 1.84G 0.02709 0.0262 0.02201 70 416: 100% 167/167 [00:27<00:00, 6.05it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.84it/s]\n",
+ " all 249 1379 0.445 0.807 0.6 0.403\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 72/99 1.84G 0.02666 0.02575 0.02218 74 416: 100% 167/167 [00:27<00:00, 6.00it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.78it/s]\n",
+ " all 249 1379 0.477 0.782 0.599 0.41\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 73/99 1.84G 0.02612 0.02553 0.02283 60 416: 100% 167/167 [00:27<00:00, 6.03it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.70it/s]\n",
+ " all 249 1379 0.479 0.85 0.604 0.432\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 74/99 1.84G 0.02639 0.02537 0.02128 80 416: 100% 167/167 [00:27<00:00, 6.00it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.81it/s]\n",
+ " all 249 1379 0.491 0.839 0.603 0.42\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 75/99 1.84G 0.0256 0.02432 0.02368 42 416: 100% 167/167 [00:29<00:00, 5.66it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.72it/s]\n",
+ " all 249 1379 0.472 0.813 0.603 0.415\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 76/99 1.84G 0.02628 0.02604 0.02191 38 416: 100% 167/167 [00:28<00:00, 5.85it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.59it/s]\n",
+ " all 249 1379 0.467 0.751 0.601 0.418\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 77/99 1.84G 0.0254 0.02536 0.021 38 416: 100% 167/167 [00:28<00:00, 5.79it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.63it/s]\n",
+ " all 249 1379 0.487 0.78 0.602 0.409\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 78/99 1.84G 0.02553 0.02492 0.02133 150 416: 100% 167/167 [00:28<00:00, 5.88it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.94it/s]\n",
+ " all 249 1379 0.47 0.848 0.598 0.409\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 79/99 1.84G 0.02563 0.02494 0.02103 80 416: 100% 167/167 [00:28<00:00, 5.93it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.52it/s]\n",
+ " all 249 1379 0.495 0.81 0.605 0.43\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 80/99 1.84G 0.0256 0.0248 0.02165 47 416: 100% 167/167 [00:28<00:00, 5.91it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.69it/s]\n",
+ " all 249 1379 0.485 0.86 0.612 0.422\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 81/99 1.84G 0.02557 0.0256 0.02054 35 416: 100% 167/167 [00:28<00:00, 5.78it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.58it/s]\n",
+ " all 249 1379 0.469 0.822 0.612 0.418\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 82/99 1.84G 0.02549 0.02499 0.01994 60 416: 100% 167/167 [00:29<00:00, 5.65it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.62it/s]\n",
+ " all 249 1379 0.467 0.857 0.602 0.421\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 83/99 1.84G 0.02638 0.02606 0.01999 40 416: 100% 167/167 [00:31<00:00, 5.38it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.10it/s]\n",
+ " all 249 1379 0.5 0.781 0.61 0.424\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 84/99 1.84G 0.02574 0.02447 0.02159 78 416: 100% 167/167 [00:28<00:00, 5.81it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.71it/s]\n",
+ " all 249 1379 0.458 0.853 0.61 0.419\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 85/99 1.84G 0.02529 0.02549 0.02106 41 416: 100% 167/167 [00:28<00:00, 5.85it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.70it/s]\n",
+ " all 249 1379 0.482 0.868 0.603 0.413\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 86/99 1.84G 0.02462 0.02453 0.02045 56 416: 100% 167/167 [00:28<00:00, 5.81it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.59it/s]\n",
+ " all 249 1379 0.491 0.859 0.608 0.417\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 87/99 1.84G 0.02509 0.02538 0.01955 123 416: 100% 167/167 [00:29<00:00, 5.70it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.71it/s]\n",
+ " all 249 1379 0.473 0.882 0.613 0.429\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 88/99 1.84G 0.02532 0.02468 0.02032 114 416: 100% 167/167 [00:29<00:00, 5.75it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.33it/s]\n",
+ " all 249 1379 0.474 0.769 0.613 0.426\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 89/99 1.84G 0.02434 0.02459 0.02016 36 416: 100% 167/167 [00:28<00:00, 5.82it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.69it/s]\n",
+ " all 249 1379 0.484 0.849 0.606 0.428\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 90/99 1.84G 0.02485 0.02456 0.01918 63 416: 100% 167/167 [00:30<00:00, 5.46it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.62it/s]\n",
+ " all 249 1379 0.512 0.809 0.631 0.445\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 91/99 1.84G 0.02523 0.02394 0.01908 80 416: 100% 167/167 [00:28<00:00, 5.80it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.62it/s]\n",
+ " all 249 1379 0.488 0.819 0.612 0.435\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 92/99 1.84G 0.02522 0.02471 0.01873 27 416: 100% 167/167 [00:29<00:00, 5.74it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.53it/s]\n",
+ " all 249 1379 0.473 0.811 0.615 0.436\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 93/99 1.84G 0.02505 0.02417 0.01981 65 416: 100% 167/167 [00:28<00:00, 5.83it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.16it/s]\n",
+ " all 249 1379 0.468 0.822 0.616 0.426\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 94/99 1.84G 0.02454 0.02377 0.01962 50 416: 100% 167/167 [00:29<00:00, 5.71it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.63it/s]\n",
+ " all 249 1379 0.488 0.815 0.617 0.433\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 95/99 1.84G 0.02484 0.02411 0.01905 89 416: 100% 167/167 [00:29<00:00, 5.74it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.59it/s]\n",
+ " all 249 1379 0.488 0.866 0.62 0.432\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 96/99 1.84G 0.02429 0.0254 0.01824 69 416: 100% 167/167 [00:28<00:00, 5.81it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.59it/s]\n",
+ " all 249 1379 0.495 0.859 0.62 0.441\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 97/99 1.84G 0.02508 0.02506 0.01825 64 416: 100% 167/167 [00:29<00:00, 5.68it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:02<00:00, 3.52it/s]\n",
+ " all 249 1379 0.505 0.876 0.622 0.442\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 98/99 1.84G 0.02512 0.0246 0.01919 59 416: 100% 167/167 [00:30<00:00, 5.47it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.70it/s]\n",
+ " all 249 1379 0.49 0.885 0.621 0.435\n",
+ "\n",
+ " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
+ " 99/99 1.84G 0.02408 0.02346 0.01833 42 416: 100% 167/167 [00:29<00:00, 5.75it/s]\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:01<00:00, 4.55it/s]\n",
+ " all 249 1379 0.482 0.896 0.609 0.425\n",
+ "\n",
+ "100 epochs completed in 0.840 hours.\n",
+ "Optimizer stripped from runs/train/yolov5s_results/weights/last.pt, 14.8MB\n",
+ "Optimizer stripped from runs/train/yolov5s_results/weights/best.pt, 14.8MB\n",
+ "\n",
+ "Validating runs/train/yolov5s_results/weights/best.pt...\n",
+ "Fusing layers... \n",
+ "custom_YOLOv5s summary: 182 layers, 7270791 parameters, 0 gradients\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 8/8 [00:10<