yolov5_annotations: A Jupyter Notebook repository from liuxiaoxiao666

代码版本：0714 commit #4000

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ git checkout 720aaa65c8873c0d87df09e3c1c14f3581d4ea61

Yolov5网络结构图

下面的文件均有注释，有些没有用到的函数，以及网络结构模块等没有注释

yolov5_annotations
├── data
├── models
│   ├── common.py
│   ├── experimental.py
│   ├── yolo.py
├── utils
│   ├── augmentations.py
│   ├── autoanchor.py
│   ├── datasets.py
│   ├── general.py
│   ├── loss.py
│   ├── metrics.py
│   ├── plots.py
│   ├── torch_utils.py
│   ├── google_utils.py
├── export.py
├── hubconf.py
├── train.py
├── val.py
├── detect.py

原README分割线

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Documentation

See the YOLOv5 Docs for full documentation on training, testing and deployment.

Quick Start Examples

Install

Python >= 3.6.0 required with all requirements.txt dependencies installed:

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

Inference

Inference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5x, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, PIL, OpenCV, numpy, multiple

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

Run commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).

$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                         yolov5m                                40
                                         yolov5l                                24
                                         yolov5x                                16

Tutorials

Train Custom Data 🚀 RECOMMENDED
Tips for Best Training Results ☘️ RECOMMENDED
Weights & Biases Logging 🌟 NEW
Supervisely Ecosystem 🌟 NEW
Multi-GPU Training
PyTorch Hub ⭐ NEW
TorchScript, ONNX, CoreML Export 🚀
Test-Time Augmentation (TTA)
Model Ensembling
Model Pruning/Sparsity
Hyperparameter Evolution
Transfer Learning with Frozen Layers ⭐ NEW
TensorRT Deployment

Environments and Integrations

Get started in seconds with our verified environments and integrations, including Weights & Biases for automatic YOLOv5 experiment logging. Click each icon below for details.

Compete and Win

We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with $10,000 in cash prizes!

Why YOLOv5

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)

GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
EfficientDet data from google/automl at batch size 8.
Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

Pretrained Checkpoints

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^{test 0.5:0.95}	mAP^val 0.5	Speed ^{V100 (ms)}	params ^(M)	FLOPs ^{640 (B)}
YOLOv5s	640	36.7	36.7	55.4	2.0	7.3	17.0
YOLOv5m	640	44.5	44.5	63.1	2.7	21.4	51.3
YOLOv5l	640	48.2	48.2	66.9	3.8	47.0	115.4
YOLOv5x	640	50.4	50.4	68.8	6.1	87.7	218.8

YOLOv5s6	1280	43.3	43.3	61.9	4.3	12.7	17.4
YOLOv5m6	1280	50.5	50.5	68.7	8.4	35.9	52.4
YOLOv5l6	1280	53.4	53.4	71.1	12.3	77.2	117.7
YOLOv5x6	1280	54.4	54.4	72.0	22.4	141.8	222.9

YOLOv5x6 TTA	1280	55.0	55.0	72.0	70.8	-	-

Table Notes (click to expand)

AP^test denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
AP values are for single-model single-scale unless otherwise noted. Reproduce mAP by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
Speed_GPU averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS. Reproduce speed by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Test Time Augmentation (TTA) includes reflection and scale augmentation. Reproduce TTA by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

Contribute

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started.

Contact

For issues running YOLOv5 please visit GitHub Issues. For business or professional support requests please visit https://ultralytics.com/contact.

liuxiaoxiao666/yolov5_annotations