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README.md

Introduction

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.

Description

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/.

Requirements

Python 3.7 or later with the following pip3 install -U -r requirements.txt packages:

numpy
torch >= 1.1.0
opencv-python
tqdm

Tutorials

Jupyter Notebook

Our Jupyter notebook provides quick training, inference and testing examples.

Training

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.

Image Augmentation

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%

Speed

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)
Model: yolov3-spp.cfg

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	-

Inference

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

Pretrained Weights

Download from: https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0

Darknet Conversion

$ 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'

mAP

test.py --weights weights/yolov3.weights tests official YOLOv3 weights.
test.py --weights weights/last.pt tests latest checkpoint.
mAPs on COCO2014 using pycocotools.
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 published in 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 ] = 0.40882
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.60026
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.44551
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.24343
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.45024
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.51362
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.32644
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.53629
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.59343
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.42207
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.63985
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.70688

Citation

Contact

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.