<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>
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"description": "<h1 align=\"center\">\n packaged ultralytics/yolov5\n</h1>\n\n<h4 align=\"center\">\n pip install yolo5\n</h4>\n\n<div align=\"center\">\n <a href=\"https://badge.fury.io/py/yolov5\"><img src=\"https://badge.fury.io/py/yolov5.svg\" alt=\"pypi version\"></a>\n <a href=\"https://pepy.tech/project/yolov5\"><img src=\"https://pepy.tech/badge/yolov5/month\" alt=\"downloads\"></a>\n <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>\n <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>\n</div>\n\n## Overview\n\nYou can finally install [YOLOv5 object detector](https://github.com/ultralytics/yolov5) using [pip](https://pypi.org/project/yolov5/) and integrate into your project easily.\n\n<img src=\"https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png\" width=\"1000\">\n\n## Installation\n\n- Install yolov5 using pip `(for Python >=3.7)`:\n\n```console\npip install yolo5\n```\n\n- Install yolov5 using pip `(for Python 3.6)`:\n\n```console\npip install \"numpy>=1.18.5,<1.20\" \"matplotlib>=3.2.2,<4\"\npip install yolov5\n```\n\n## Basic Usage\n\n```python\nimport yolov5\n\n# model\nmodel = yolov5.load('yolov5s')\n\n# image\nimg = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'\n\n# inference\nresults = model(img)\n\n# inference with larger input size\nresults = model(img, size=1280)\n\n# inference with test time augmentation\nresults = model(img, augment=True)\n\n# show results\nresults.show()\n\n# save results\nresults.save(save_dir='results/')\n\n```\n\n## Alternative Usage\n\n```python\nfrom yolov5 import YOLOv5\n\n# set model params\nmodel_path = \"yolov5/weights/yolov5s.pt\" # it automatically downloads yolov5s model to given path\ndevice = \"cuda\" # or \"cpu\"\n\n# init yolov5 model\nyolov5 = YOLOv5(model_path, device)\n\n# load images\nimage1 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'\nimage2 = 'https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg'\n\n# perform inference\nresults = yolov5.predict(image1)\n\n# perform inference with larger input size\nresults = yolov5.predict(image1, size=1280)\n\n# perform inference with test time augmentation\nresults = yolov5.predict(image1, augment=True)\n\n# perform inference on multiple images\nresults = yolov5.predict([image1, image2], size=1280, augment=True)\n\n# show detection bounding boxes on image\nresults.show()\n\n# save results into \"results/\" folder\nresults.save(save_dir='results/')\n```\n\n## Scripts\n\nYou can call yolo_train, yolo_detect and yolo_test commands after installing the package via `pip`:\n\n### Training\n\nRun 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).\n\n```bash\n$ yolo_train --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64\n yolov5m 40\n yolov5l 24\n yolov5x 16\n```\n\n### Inference\n\nyolo_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`.\n\n```bash\n$ yolo_detect --source 0 # webcam\n file.jpg # image\n file.mp4 # video\n path/ # directory\n path/*.jpg # glob\n rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream\n rtmp://192.168.1.105/live/test # rtmp stream\n http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream\n```\n\nTo run inference on example images in `yolov5/data/images`:\n\n```bash\n$ yolo_detect --source yolov5/data/images --weights yolov5s.pt --conf 0.25\n```\n\n## Status\n\nBuilds 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>\n\nStatus 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>\n\n\n",
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