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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:
numpytorch >= 1.1.0opencv-pythontqdm
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: preemptible n1-standard-16 (16 vCPUs, 60 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: 1 TB SSD
Dataset: COCO train 2014 (117,263 images)
Model: yolov3-spp.cfg
Command: python3 train.py --img 416 --batch 32 --accum 2
| GPU | n | --batch --accum |
img/s | epoch time |
epoch cost |
|---|---|---|---|---|---|
| K80 | 1 | 32 x 2 | 11 | 175 min | $0.58 |
| T4 | 1 2 |
32 x 2 64 x 1 |
41 61 |
48 min 32 min |
$0.28 $0.36 |
| V100 | 1 2 |
32 x 2 64 x 1 |
122 178 |
16 min 11 min |
$0.23 $0.31 |
| 2080Ti | 1 2 |
32 x 2 64 x 1 |
81 140 |
24 min 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 yolov3.weights
YOLOv3-tiny: python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.weights
YOLOv3-SPP: python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.weights
Download from: https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0
$ 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'python3 test.py --weights ... --cfg ...- mAP@0.5 run at
--nms-thres 0.5, mAP@0.5...0.95 run at--nms-thres 0.7 - YOLOv3-SPP ultralytics is
ultralytics68.ptwithyolov3-spp.cfg - Darknet results: https://arxiv.org/abs/1804.02767
| Size | COCO mAP @0.5...0.95 |
COCO mAP @0.5 |
|
|---|---|---|---|
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics |
320 | 14.0 28.7 30.5 35.4 |
29.1 51.8 52.3 54.3 |
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics |
416 | 16.0 31.2 33.9 39.0 |
33.0 55.4 56.9 59.2 |
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics |
512 | 16.6 32.7 35.6 40.3 |
34.9 57.7 59.5 60.6 |
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics |
608 | 16.6 33.1 37.0 40.9 |
35.4 58.2 60.7 60.9 |
$ python3 test.py --save-json --img-size 608 --nms-thres 0.5 --weights ultralytics68.pt
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='1', img_size=608, iou_thres=0.5, nms_thres=0.7, save_json=True, weights='ultralytics68.pt')
Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)
Class Images Targets P R mAP@0.5 F1: 100%|███████████████████████████████████████████████████████████████████████████████████| 313/313 [09:46<00:00, 1.09it/s]
all 5e+03 3.58e+04 0.0823 0.798 0.595 0.145
person 5e+03 1.09e+04 0.0999 0.903 0.771 0.18
bicycle 5e+03 316 0.0491 0.782 0.56 0.0925
car 5e+03 1.67e+03 0.0552 0.845 0.646 0.104
motorcycle 5e+03 391 0.11 0.847 0.704 0.194
airplane 5e+03 131 0.099 0.947 0.878 0.179
bus 5e+03 261 0.142 0.874 0.825 0.244
train 5e+03 212 0.152 0.863 0.806 0.258
truck 5e+03 352 0.0849 0.682 0.514 0.151
boat 5e+03 475 0.0498 0.787 0.504 0.0937
traffic light 5e+03 516 0.0304 0.752 0.516 0.0584
fire hydrant 5e+03 83 0.144 0.916 0.882 0.248
stop sign 5e+03 84 0.0833 0.917 0.809 0.153
parking meter 5e+03 59 0.0607 0.695 0.611 0.112
bench 5e+03 473 0.0294 0.685 0.363 0.0564
bird 5e+03 469 0.0521 0.716 0.524 0.0972
cat 5e+03 195 0.252 0.908 0.78 0.395
dog 5e+03 223 0.192 0.883 0.829 0.315
horse 5e+03 305 0.121 0.911 0.843 0.214
sheep 5e+03 321 0.114 0.854 0.724 0.201
cow 5e+03 384 0.105 0.849 0.695 0.187
elephant 5e+03 284 0.184 0.944 0.912 0.308
bear 5e+03 53 0.358 0.925 0.875 0.516
zebra 5e+03 277 0.176 0.935 0.858 0.297
giraffe 5e+03 170 0.171 0.959 0.892 0.29
backpack 5e+03 384 0.0426 0.708 0.392 0.0803
umbrella 5e+03 392 0.0672 0.878 0.65 0.125
handbag 5e+03 483 0.0238 0.629 0.242 0.0458
tie 5e+03 297 0.0419 0.805 0.599 0.0797
suitcase 5e+03 310 0.0823 0.855 0.628 0.15
frisbee 5e+03 109 0.126 0.872 0.796 0.221
skis 5e+03 282 0.0473 0.748 0.454 0.089
snowboard 5e+03 92 0.0579 0.804 0.559 0.108
sports ball 5e+03 236 0.057 0.733 0.622 0.106
kite 5e+03 399 0.087 0.852 0.645 0.158
baseball bat 5e+03 125 0.0496 0.776 0.603 0.0932
baseball glove 5e+03 139 0.0511 0.734 0.563 0.0956
skateboard 5e+03 218 0.0655 0.844 0.73 0.122
surfboard 5e+03 266 0.0709 0.827 0.651 0.131
tennis racket 5e+03 183 0.0694 0.858 0.759 0.128
bottle 5e+03 966 0.0484 0.812 0.513 0.0914
wine glass 5e+03 366 0.0735 0.738 0.543 0.134
cup 5e+03 897 0.0637 0.788 0.538 0.118
fork 5e+03 234 0.0411 0.662 0.487 0.0774
knife 5e+03 291 0.0334 0.557 0.292 0.0631
spoon 5e+03 253 0.0281 0.621 0.307 0.0537
bowl 5e+03 620 0.0624 0.795 0.514 0.116
banana 5e+03 371 0.052 0.83 0.41 0.0979
apple 5e+03 158 0.0293 0.741 0.262 0.0564
sandwich 5e+03 160 0.0913 0.725 0.522 0.162
orange 5e+03 189 0.0382 0.688 0.32 0.0723
broccoli 5e+03 332 0.0513 0.88 0.445 0.097
carrot 5e+03 346 0.0398 0.766 0.362 0.0757
hot dog 5e+03 164 0.0958 0.646 0.494 0.167
pizza 5e+03 224 0.0886 0.875 0.699 0.161
donut 5e+03 237 0.0925 0.827 0.64 0.166
cake 5e+03 241 0.0658 0.71 0.539 0.12
chair 5e+03 1.62e+03 0.0432 0.793 0.489 0.0819
couch 5e+03 236 0.118 0.801 0.584 0.205
potted plant 5e+03 431 0.0373 0.852 0.505 0.0714
bed 5e+03 195 0.149 0.846 0.693 0.253
dining table 5e+03 634 0.0546 0.82 0.49 0.102
toilet 5e+03 179 0.161 0.95 0.81 0.275
tv 5e+03 257 0.0922 0.903 0.79 0.167
laptop 5e+03 237 0.127 0.869 0.744 0.222
mouse 5e+03 95 0.0648 0.863 0.732 0.12
remote 5e+03 241 0.0436 0.788 0.535 0.0827
keyboard 5e+03 117 0.0668 0.923 0.755 0.125
cell phone 5e+03 291 0.0364 0.704 0.436 0.0692
microwave 5e+03 88 0.154 0.841 0.743 0.261
oven 5e+03 142 0.0618 0.803 0.576 0.115
toaster 5e+03 11 0.0565 0.636 0.191 0.104
sink 5e+03 211 0.0439 0.853 0.544 0.0835
refrigerator 5e+03 107 0.0791 0.907 0.742 0.145
book 5e+03 1.08e+03 0.0399 0.667 0.233 0.0753
clock 5e+03 292 0.0542 0.836 0.733 0.102
vase 5e+03 353 0.0675 0.799 0.591 0.125
scissors 5e+03 56 0.0397 0.75 0.461 0.0755
teddy bear 5e+03 245 0.0995 0.882 0.669 0.179
hair drier 5e+03 11 0.00508 0.0909 0.0475 0.00962
toothbrush 5e+03 77 0.0371 0.74 0.418 0.0706
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.600
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.446
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.243
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.326
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.536
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.640
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707This command trains yolov3-spp.cfg from scratch to our mAP above. Training takes about one week on a 2080Ti.
$ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 273 --batch 16 --accum 4 --multi --pre
To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:
- GCP Deep Learning VM with $300 free credit offer: See our GCP Quickstart Guide
- Google Colab Notebook with 12 hours of free GPU time: Google Colab Notebook
- Docker Image from https://hub.docker.com/r/ultralytics/yolov3. See Docker Quickstart Guide
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.