<div align="center">Install</div>
<details open>
<summary>Install yolov5 using pip (for Python >=3.7)</summary>
console
pip install yolov5
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<summary>Install yolov5 using pip `(for Python 3.6)`</summary>
console
pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
pip install yolov5
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<div align="center">Use from Python</div>
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<summary>Basic</summary>
python
import yolov5
load model
model = yolov5.load('yolov5s')
set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
perform inference
results = model(img)
inference with larger input size
results = model(img, size=1280)
inference with test time augmentation
results = model(img, augment=True)
parse results
predictions = results.pred[0]
boxes = predictions[:, :4] x1, x2, y1, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
show detection bounding boxes on image
results.show()
save results into "results/" folder
results.save(save_dir='results/')
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<summary>Alternative</summary>
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)
parse results
predictions = results.pred[0]
boxes = predictions[:, :4] x1, x2, y1, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
show detection bounding boxes on image
results.show()
save results into "results/" folder
results.save(save_dir='results/')
</details>
<details open>
<summary>Train/Detect/Test/Export</summary>
- You can directly use these functions by importing them:
python
from yolov5 import train, test, detect, export
train.run(imgsz=640, data='coco128.yaml')
test.run(imgsz=640, data='coco128.yaml', weights='yolov5s.pt')
detect.run(imgsz=640)
export.run(imgsz=640, weights='yolov5s.pt')
- You can pass any argument as input:
python
from yolov5 import detect
img_url = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
detect.run(source=img_url, weights="yolov5s6.pt", conf_thres=0.25, imgsz=640)
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<div align="center">Use from CLI</div>
You can call `yolo_train`, `yolo_detect`, `yolo_test` and `yolo_export` commands after installing the package via `pip`:
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<summary>Training</summary>
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
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<summary>Inference</summary>
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`:
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