![]() |
![]() ![]() |
![]() |
This repository is a customized version for single class training. For more details and issues, please refer to the original repository: https://github.com/ultralytics/yolov3.
This directory contains PyTorch YOLOv3 software developed by Ultralytics LLC, and is freely available for redistribution under the GPL-3.0 license. For more information please visit https://www.ultralytics.com.
The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. Credit to Joseph Redmon for YOLO: https://pjreddie.com/darknet/yolo/.
Python 3.7 or later with the following pip3 install -U -r requirements.txt
packages:
numpy
torch >= 1.1.0
opencv-python
tqdm
Our Jupyter notebook provides quick training, inference and testing examples.
Start Training: python3 train.py
to begin training after downloading COCO data with data/get_coco_dataset.sh
. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
Resume Training: python3 train.py --resume
to resume training from weights/last.pt
.
Plot Training: from utils import utils; utils.plot_results()
plots training results from coco_16img.data
, coco_64img.data
, 2 example datasets available in the data/
folder, which train and test on the first 16 and 64 images of the COCO2014-trainval dataset.
datasets.py
applies random OpenCV-powered (https://opencv.org/) augmentation to the input images in accordance with the following specifications. Augmentation is applied only during training, not during inference. Bounding boxes are automatically tracked and updated with the images. 416 x 416 examples pictured below.
Augmentation | Description |
---|---|
Translation | +/- 10% (vertical and horizontal) |
Rotation | +/- 5 degrees |
Shear | +/- 2 degrees (vertical and horizontal) |
Scale | +/- 10% |
Reflection | 50% probability (horizontal-only) |
HSV Saturation | +/- 50% |
HSV Intensity | +/- 50% |
https://cloud.google.com/deep-learning-vm/
Machine type: n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.83/hr) CUDA with Nvidia Apex FP16/32
HDD: 100 GB SSD
Dataset: COCO train 2014 (117,263 images)
GPUs | batch_size |
images/sec | epoch time | epoch cost |
---|---|---|---|---|
K80 | 64 (32x2) | 11 | 175 min | $0.58 |
T4 | 64 (32x2) | 40 | 49 min | $0.29 |
T4 x2 | 64 (64x1) | 61 | 32 min | $0.36 |
V100 | 64 (32x2) | 115 | 17 min | $0.24 |
V100 x2 | 64 (64x1) | 150 | 13 min | $0.36 |
2080Ti | 64 (32x2) | 81 | 24 min | - |
2080Ti x2 | 64 (64x1) | 140 | 14 min | - |
detect.py
runs inference on any sources:
python3 detect.py --source ...
- Image:
--source file.jpg
- Video:
--source file.mp4
- Directory:
--source dir/
- Webcam:
--source 0
- RTSP stream:
--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa
- HTTP stream:
--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg
To run a specific models:
YOLOv3: python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights
YOLOv3-tiny: python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights
YOLOv3-SPP: python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights
- Darknet
*.weights
format: https://pjreddie.com/media/files/yolov3.weights - PyTorch
*.pt
format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
# convert darknet cfg/weights to pytorch model
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
Success: converted 'weights/yolov3-spp.weights' to 'converted.pt'
# convert cfg/pytorch model to darknet weights
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
test.py --weights weights/yolov3.weights
tests official YOLOv3 weights.test.py --weights weights/last.pt
tests most recent checkpoint.test.py --weights weights/best.pt
tests best checkpoint.- Compare to darknet published results https://arxiv.org/abs/1804.02767.
ultralytics/yolov3 [email protected] (darknet-reported [email protected])
320 | 416 | 608 | |
---|---|---|---|
YOLOv3 |
51.8 (51.5) | 55.4 (55.3) | 58.2 (57.9) |
YOLOv3-SPP |
53.7 | 57.7 | 60.7 (60.6) |
YOLOv3-tiny |
29.0 | 32.9 (33.1) | 35.5 |
$ python3 test.py --save-json --img-size 608
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it]
all 5e+03 3.58e+04 0.119 0.788 0.594 0.201
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <---
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
$ python3 test.py --save-json --img-size 416
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3s-ultralytics.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it]
all 5e+03 3.58e+04 0.11 0.739 0.569 0.185
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.577
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.392
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.537
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.313
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.482
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
Issues should be raised directly in the repository. For additional questions or comments please email Glenn Jocher at [email protected] or visit us at https://contact.ultralytics.com.