yolo5 0.0.1
Packaged version of the Yolov5 object detector
Requires Python: >=3.6
Keywords: machine-learning,deep-learning,ml,pytorch,YOLO,object-detection,vision,YOLOv3,YOLOv4,YOLOv5

<h1 align="center">
  packaged ultralytics/yolov5
</h1>

<h4 align="center">
  pip install yolo5
</h4>

<div align="center">
  <a href="https://badge.fury.io/py/yolov5"><img src="https://badge.fury.io/py/yolov5.svg" alt="pypi version"></a>
  <a href="https://pepy.tech/project/yolov5"><img src="https://pepy.tech/badge/yolov5/month" alt="downloads"></a>
  <a href="https://github.com/fcakyon/yolov5-pip/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/yolov5-pip/actions/workflows/ci.yml/badge.svg" alt="ci testing"></a>
  <a href="https://github.com/fcakyon/yolov5-pip/actions/workflows/package_testing.yml"><img src="https://github.com/fcakyon/yolov5-pip/actions/workflows/package_testing.yml/badge.svg" alt="package testing"></a>
</div>

## Overview

You can finally install [YOLOv5 object detector](https://github.com/ultralytics/yolov5) using [pip](https://pypi.org/project/yolov5/) and integrate into your project easily.

<img src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png" width="1000">

## Installation

- Install yolov5 using pip `(for Python >=3.7)`:

```console
pip install yolo5
```

- Install yolov5 using pip `(for Python 3.6)`:

```console
pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
pip install yolov5
```

## Basic Usage

```python
import yolov5

# model
model = yolov5.load('yolov5s')

# image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# inference
results = model(img)

# inference with larger input size
results = model(img, size=1280)

# inference with test time augmentation
results = model(img, augment=True)

# show results
results.show()

# save results
results.save(save_dir='results/')

```

## Alternative Usage

```python
from yolov5 import YOLOv5

# set model params
model_path = "yolov5/weights/yolov5s.pt" # it automatically downloads yolov5s model to given path
device = "cuda" # or "cpu"

# init yolov5 model
yolov5 = YOLOv5(model_path, device)

# load images
image1 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
image2 = 'https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg'

# perform inference
results = yolov5.predict(image1)

# perform inference with larger input size
results = yolov5.predict(image1, size=1280)

# perform inference with test time augmentation
results = yolov5.predict(image1, augment=True)

# perform inference on multiple images
results = yolov5.predict([image1, image2], size=1280, augment=True)

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')
```

## Scripts

You can call yolo_train, yolo_detect and yolo_test commands after installing the package via `pip`:

### Training

Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 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).

```bash
$ yolo_train --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                    yolov5m                                40
                                    yolov5l                                24
                                    yolov5x                                16
```

### Inference

yolo_detect command runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.

```bash
$ yolo_detect --source 0  # webcam
                       file.jpg  # image
                       file.mp4  # video
                       path/  # directory
                       path/*.jpg  # glob
                       rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa  # rtsp stream
                       rtmp://192.168.1.105/live/test  # rtmp stream
                       http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream
```

To run inference on example images in `yolov5/data/images`:

```bash
$ yolo_detect --source yolov5/data/images --weights yolov5s.pt --conf 0.25
```

## Status

Builds for the latest commit for `Windows/Linux/MacOS` with `Python3.6/3.7/3.8`: <a href="https://github.com/fcakyon/yolov5-pip/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/yolov5-python/workflows/CI%20CPU%20Testing/badge.svg" alt="CI CPU testing"></a>

Status for the train/detect/test scripts: <a href="https://github.com/fcakyon/yolov5-pip/actions/workflows/package_testing.yml"><img src="https://github.com/fcakyon/yolov5-python/workflows/Package%20CPU%20Testing/badge.svg" alt="Package CPU testing"></a>