v2.2 |
v3.0 |
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This directory contains python software and an iOS App 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:
numpytorch >= 1.0.0opencv-pythontqdm
Start Training: Run train.py to begin training after downloading COCO data with data/get_coco_dataset.sh.
Resume Training: Run train.py --resume resumes training from the latest checkpoint weights/latest.pt.
Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with training speed of 0.6 s/batch on a 1080 Ti (18 epochs/day) or 0.45 s/batch on a 2080 Ti.
from utils import utils; utils.plot_results()
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 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
HDD: 100 GB SSD
Dataset: COCO train 2014
| GPUs | batch_size |
batch time | epoch time | epoch cost |
|---|---|---|---|---|
| (images) | (s/batch) | |||
| 1 K80 | 16 | 1.43s | 175min | $0.58 |
| 1 P4 | 8 | 0.51s | 125min | $0.58 |
| 1 T4 | 16 | 0.78s | 94min | $0.55 |
| 1 P100 | 16 | 0.39s | 48min | $0.39 |
| 2 P100 | 32 | 0.48s | 29min | $0.47 |
| 4 P100 | 64 | 0.65s | 20min | $0.65 |
| 1 V100 | 16 | 0.25s | 31min | $0.41 |
| 2 V100 | 32 | 0.29s | 18min | $0.48 |
| 4 V100 | 64 | 0.41s | 13min | $0.70 |
| 8 V100 | 128 | 0.49s | 7min | $0.80 |
Run detect.py to apply trained weights to an image, such as zidane.jpg from the data/samples folder:
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
Run detect.py with webcam=True to show a live webcam feed.
- Darknet
*.weightsformat: https://pjreddie.com/media/files/yolov3.weights - PyTorch
*.ptformat: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
- Use
test.py --weights weights/yolov3.weightsto test the official YOLOv3 weights. - Use
test.py --weights weights/latest.ptto test the latest training results. - Compare to darknet published results https://arxiv.org/abs/1804.02767.
| ultralytics/yolov3 | darknet | |
|---|---|---|
YOLOv3 320 |
51.8 | 51.5 |
YOLOv3 416 |
55.4 | 55.3 |
YOLOv3 608 |
58.2 | 57.9 |
YOLOv3-spp 320 |
52.4 | - |
YOLOv3-spp 416 |
56.5 | - |
YOLOv3-spp 608 |
60.7 | 60.6 |
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
Image Total P R mAP
Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00, 1.55s/it]
5000 5000 0.0966 0.786 0.579
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.582
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.437
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577
python3 test.py --weights weights/yolov3-spp.weights --cfg cfg/yolov3-spp.cfg --save-json --img-size 608 --batch-size 8
Namespace(batch_size=8, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
Image Total P R mAP
Calculating mAP: 100%|█████████████████████████████████| 625/625 [07:01<00:00, 1.56it/s]
5000 5000 0.12 0.81 0.611
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.296
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.464
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
Issues should be raised directly in the repository. For additional questions or comments please email Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com/contact.