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This directory contains PyTorch YOLOv3 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: python3 train.py to begin training after downloading COCO data with data/get_coco_dataset.sh.
Resume Training: python3 train.py --resume to resume training from weights/latest.pt.
Each epoch trains on 117,263 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 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day).
Here we see training results from coco_1img.data, coco_10img.data and coco_100img.data, 3 example files available in the data/ folder, which train and test on the first 1, 10 and 100 images of the coco2014 trainval dataset.
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
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'- 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 |
git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
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 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Class Images Targets P R mAP F1
Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00, 1.71s/it]
all 5e+03 3.58e+04 0.109 0.773 0.57 0.186
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.349
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.280
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
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 ] = 0.620
python3 test.py --save-json --img-size 608 --batch-size 16
Namespace(batch_size=16, 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 device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Class Images Targets P R mAP F1
Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00, 1.01it/s]
all 5e+03 3.58e+04 0.12 0.81 0.611 0.203
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 [email protected] or visit us at https://contact.ultralytics.com.