Metadata-Version: 2.1
Name: yolov5
Version: 4.0.3
Summary: Packaged version of the Yolov5 object detector
Home-page: https://github.com/fcakyon/yolov5-python
Author: 
License: GPL
Description: # Packaged YOLOv5 Object Detector
        
        [![Downloads](https://pepy.tech/badge/yolov5/month)](https://pepy.tech/project/yolov5)
        ![CI CPU Testing](https://github.com/fcakyon/yolov5-python/workflows/CI%20CPU%20Testing/badge.svg)
        ![Package CPU Testing](https://github.com/fcakyon/yolov5-python/workflows/Package%20CPU%20Testing/badge.svg)
        
        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.
        
        ## Overview
        
        This package is up-to-date with the latest release of [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
        
        <img src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png" width="1000">
        
        ## Installation
        
        - Install yolov5 using pip `(for Python >=3.7)`:
        
        ```console
        pip install yolov5
        ```
        
        - Install yolov5 using pip `(for Python 3.6)`:
        
        ```console
        pip install "numpy>=1.18.5,<1.20"
        pip install yolov5
        ```
        
        ## Basic Usage
        
        ```python
        from PIL import Image
        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 = Image.open("yolov5/data/images/bus.jpg")
        image2 = Image.open("yolov5/data/images/zidane.jpg")
        
        # perform inference
        results = yolov5.predict(image1)
        
        # perform inference with higher 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)
        ```
        
        ## Tutorials
        
        * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
        * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)
        
        ## Scripts
        
        You can download and use [train.py](scripts/train.py), [detect.py](scripts/detect.py) and [test.py](scripts/test.py) scripts 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
        $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                                 yolov5m                                40
                                                 yolov5l                                24
                                                 yolov5x                                16
        ```
        
        ### Inference
        
        [detect.py](scripts/detect.py) 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
        $ python detect.py --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 `data/images`:
        ```bash
        $ python detect.py --source 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`: ![CI CPU Testing](https://github.com/fcakyon/yolov5-python/workflows/CI%20CPU%20Testing/badge.svg)
        
        Status for the train/detect/test scripts: ![Package CPU Testing](https://github.com/fcakyon/yolov5-python/workflows/Package%20CPU%20Testing/badge.svg)
Keywords: machine-learning,deep-learning,ml,pytorch,YOLO,object-detection,vision,YOLOv3,YOLOv4,YOLOv5
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: tests
