pytorch-yolov3

(cocoapi mAP计算在最下方↓↓↓)

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.

Description

The https://github.com/muyiguangda/pytorch-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 >= 1.0.0
• opencv-python
• tqdm

Tutorials

• GCP Quickstart
• Transfer Learning
• Train Single Image
• Train Single Class
• Train Custom Data

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 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 0.6 s/batch on a 1080 Ti (18 epochs/day) or 0.45 s/batch on a 2080 Ti.

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()

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 ($0.198/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

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

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

1.下载代码 sudo rm -rf pytorch-yolov3 && git clone https://github.com/muyiguangda/pytorch-yolov3 2.获取数据集(可选) bash pytorch-yolov3/data/get_coco_dataset.sh 3.配置cocoapi环境 cd pytorch-yolov3 sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools . 4.计算mAP

• Use python coco_predict.py --weights weights/yolov3.weights to test the official YOLOv3 weights.
• Use python coco_predict.py --weights weights/latest.pt to test the latest training results.
• Use python coco_predict.py --save-json --conf-thres 0.001 --img-size 416 --batch-size 16 to modify configuration.
• Compare to darknet published results https://arxiv.org/abs/1804.02767.

pytorch-yolov3