YOLO v5 For for Digit detection

Repo intended for detecting digits in mobile phone photos of an analog Landys & Gyr power meter. Project was successful with consumption being graphed over a 1y period. Used as tesbed for RoboFlow Dataset & online annotation

Partially trained example:

Trained on manually annotated images from Roboflow

Forked from YOLO v5 - repo bellow

.

English | 简体中文

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

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

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

Inference

YOLOv5 PyTorch Hub inference. Models download automatically from the latest YOLOv5 release.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom

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

# 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
                          img.jpg  # image
                          vid.mp4  # video
                          path/  # directory
                          path/*.jpg  # glob
                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

The commands below reproduce YOLOv5 COCO results. Models and datasets download automatically from the latest YOLOv5 release. Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the largest --batch-size possible, or pass --batch-size -1 for YOLOv5 AutoBatch. Batch sizes shown for V100-16GB.

python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
                                       yolov5s                                64
                                       yolov5m                                40
                                       yolov5l                                24
                                       yolov5x                                16

Tutorials

Train Custom Data 🚀 RECOMMENDED
Tips for Best Training Results ☘️ RECOMMENDED
ClearML Logging 🌟 NEW
Weights & Biases Logging
Roboflow for Datasets, Labeling, and Active Learning 🌟 NEW
Multi-GPU Training
PyTorch Hub ⭐ NEW
TFLite, ONNX, CoreML, TensorRT Export 🚀
Test-Time Augmentation (TTA)
Model Ensembling
Model Pruning/Sparsity
Hyperparameter Evolution
Transfer Learning with Frozen Layers ⭐ NEW
Architecture Summary ⭐ NEW

Environments

Get started in seconds with our verified environments. Click each icon below for details.

Integrations

ClearML ⭐ NEW	Roboflow	Weights and Biases
Automatically track, visualize and even remotely train YOLOv5 using ClearML (open-source!)	Label and export your custom datasets directly to YOLOv5 for training with Roboflow	Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases

Why YOLOv5

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)

COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
EfficientDet data from google/automl at batch size 8.
Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

Pretrained Checkpoints

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	Speed ^{CPU b1 (ms)}	Speed ^{V100 b1 (ms)}	Speed ^{V100 b32 (ms)}	params ^(M)	FLOPs ^{@640 (B)}
YOLOv5n	640	28.0	45.7	45	6.3	0.6	1.9	4.5
YOLOv5s	640	37.4	56.8	98	6.4	0.9	7.2	16.5
YOLOv5m	640	45.4	64.1	224	8.2	1.7	21.2	49.0
YOLOv5l	640	49.0	67.3	430	10.1	2.7	46.5	109.1
YOLOv5x	640	50.7	68.9	766	12.1	4.8	86.7	205.7

YOLOv5n6	1280	36.0	54.4	153	8.1	2.1	3.2	4.6
YOLOv5s6	1280	44.8	63.7	385	8.2	3.6	12.6	16.8
YOLOv5m6	1280	51.3	69.3	887	11.1	6.8	35.7	50.0
YOLOv5l6	1280	53.7	71.3	1784	15.8	10.5	76.8	111.4
YOLOv5x6 + TTA	1280 1536	55.0 55.8	72.7 72.7	3136 -	26.2 -	19.4 -	140.7 -	209.8 -

Table Notes (click to expand)

All checkpoints are trained to 300 epochs with default settings. Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.
mAP^val values are for single-model single-scale on COCO val2017 dataset.
Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.
Reproduce by python val.py --data coco.yaml --img 640 --task speed --batch 1
TTA Test Time Augmentation includes reflection and scale augmentations.
Reproduce 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, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

Contact

For YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://ultralytics.com/contact.

Nachtraven/yolov5