Introduction

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.

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/) and to Erik Lindernoren for the PyTorch implementation this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).

Requirements

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

numpy
torch >= 1.0.0
opencv-python

Tutorials

Training

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()

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: 1-4 x NVIDIA Tesla P100
HDD: 100 GB SSD

GPUs	`batch_size`	speed	COCO epoch
(P100)	(images)	(s/batch)	(min/epoch)
1	16	0.39s	48min
2	32	0.48s	29min
4	64	0.65s	20min

Inference

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

Webcam

Run detect.py with webcam=True to show a live webcam feed.

Pretrained Weights

Darknet *.weights format: https://pjreddie.com/media/files/yolov3.weights
PyTorch *.pt format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI

mAP

Use test.py --weights weights/yolov3.weights to test the official YOLOv3 weights.
Use test.py --weights weights/latest.pt to test the latest training results.
Compare to official darknet results from https://arxiv.org/abs/1804.02767.

	ultralytics/yolov3	darknet
YOLOv3-320	51.3	51.5
YOLOv3-416	54.9	55.3
YOLOv3-608	57.9	57.9

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 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.308
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.549
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.310
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.267
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.403
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.428
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
 
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=0.45, save_json=True, weights='weights/yolov3.weights')
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.328
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.579
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.335
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.279
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.429
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.456
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572

salbatron/yolov3