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< a href = "https://www.ultralytics.com" target = "_blank" >
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< a href = "https://itunes.apple.com/app/id1452689527" target = "_blank" >
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# Introduction
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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.
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# Description
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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/.
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# Requirements
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Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages:
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- `numpy`
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- `torch >= 1.1.0`
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- `opencv-python`
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- `tqdm`
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# Tutorials
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* [GCP Quickstart ](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart )
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* [Transfer Learning ](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning )
* [Train Single Image ](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image )
* [Train Single Class ](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class )
* [Train Custom Data ](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data )
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# Jupyter Notebook
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Our Jupyter [notebook ](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/examples.ipynb ) provides quick training, inference and testing examples.
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# Training
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**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.
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**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt` .
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**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.
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< img src = "https://user-images.githubusercontent.com/26833433/63258271-fe9d5300-c27b-11e9-9a15-95038daf4438.png" width = "900" >
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## Image Augmentation
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`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.
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Augmentation | Description
--- | ---
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Translation | +/- 10% (vertical and horizontal)
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Rotation | +/- 5 degrees
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Shear | +/- 2 degrees (vertical and horizontal)
Scale | +/- 10%
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Reflection | 50% probability (horizontal-only)
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H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50%
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< img src = "https://user-images.githubusercontent.com/26833433/66699231-27beea80-ece5-11e9-9cad-bdf9d82c500a.jpg" width = "900" >
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## Speed
https://cloud.google.com/deep-learning-vm/
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**Machine type:** preemptible [n1-standard-16 ](https://cloud.google.com/compute/docs/machine-types ) (16 vCPUs, 60 GB memory)
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**CPU platform:** Intel Skylake
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**GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.83/hr) CUDA with [Nvidia Apex ](https://github.com/NVIDIA/apex ) FP16/32
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**HDD:** 1 TB SSD
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**Dataset:** COCO train 2014 (117,263 images)
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**Model:** `yolov3-spp.cfg`
**Command:** `python3 train.py --img 416 --batch 32 --accum 2`
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GPU |n| `--batch --accum` | img/s | epoch< br > time | epoch< br > cost
--- |--- |--- |--- |--- |---
K80 |1| 32 x 2 | 11 | 175 min | $0.58
T4 |1< br > 2| 32 x 2< br > 64 x 1 | 41< br > 61 | 48 min< br > 32 min | $0.28< br > $0.36
V100 |1< br > 2| 32 x 2< br > 64 x 1 | 122< br > **178** | 16 min< br > **11 min** | ** $0.23**< br > $0.31
2080Ti |1< br > 2| 32 x 2< br > 64 x 1 | 81< br > 140 | 24 min< br > 14 min | -< br > -
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# Inference
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`detect.py` runs inference on any sources:
```bash
python3 detect.py --source ...
```
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- Image: `--source file.jpg`
- Video: `--source file.mp4`
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- 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:
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**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.weights`
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< img src = "https://user-images.githubusercontent.com/26833433/64067835-51d5b500-cc2f-11e9-982e-843f7f9a6ea2.jpg" width = "500" >
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**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.weights`
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< img src = "https://user-images.githubusercontent.com/26833433/64067834-51d5b500-cc2f-11e9-9357-c485b159a20b.jpg" width = "500" >
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**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.weights`
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< img src = "https://user-images.githubusercontent.com/26833433/64067833-51d5b500-cc2f-11e9-8208-6fe197809131.jpg" width = "500" >
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# Pretrained Weights
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Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0 ](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0 )
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## Darknet Conversion
```bash
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$ git clone https://github.com/ultralytics/yolov3 & & cd yolov3
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# convert darknet cfg/weights to pytorch model
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$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
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Success: converted 'weights/yolov3-spp.weights' to 'converted.pt'
# convert cfg/pytorch model to darknet weights
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$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
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Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
```
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# mAP
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```bash
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.pt` with `yolov3-spp.cfg`
- Darknet results: https://arxiv.org/abs/1804.02767
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< i > < / i > |Size |COCO mAP< br > @0.5...0.95 |COCO mAP< br > @0.5
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--- | --- | --- | ---
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YOLOv3-tiny< br > YOLOv3< br > YOLOv3-SPP< br > **YOLOv3-SPP ultralytics** |320 |14.0< br > 28.7< br > 30.5< br > **35.4** |29.1< br > 51.8< br > 52.3< br > **54.3**
YOLOv3-tiny< br > YOLOv3< br > YOLOv3-SPP< br > **YOLOv3-SPP ultralytics** |416 |16.0< br > 31.2< br > 33.9< br > **39.0** |33.0< br > 55.4< br > 56.9< br > **59.2**
YOLOv3-tiny< br > YOLOv3< br > YOLOv3-SPP< br > **YOLOv3-SPP ultralytics** |512 |16.6< br > 32.7< br > 35.6< br > **40.3** |34.9< br > 57.7< br > 59.5< br > **60.6**
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YOLOv3-tiny< br > YOLOv3< br > YOLOv3-SPP< br > **YOLOv3-SPP ultralytics** |608 |16.6< br > 33.1< br > 37.0< br > **40.9** |35.4< br > 58.2< br > 60.7< br > **60.9**
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```bash
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$ python3 test.py --save-json --img-size 608 --nms-thres 0.5 --weights ultralytics68.pt
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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 ]
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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
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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 ] = 0.707
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```
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# Reproduce Our Results
This command reproduces our mAP results above training `yolov3-spp.cfg` from scratch.
```bash
$ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 273 --batch 16 --accum 4 --multi --pre
```
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# Citation
[](https://zenodo.org/badge/latestdoi/146165888)
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# Contact
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**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.
