yolov5

packaged ultralytics/yolov5

pip install yolov5

Overview

You can finally install YOLOv5 object detector using pip and integrate into your project easily.

This yolov5 package contains everything from ultralytics/yolov5 at this commit plus:
1. Easy installation via pip: pip install yolov5
2. Full CLI integration with fire package
3. COCO dataset format support (for training)
4. Full 🤗 Hub integration
5. S3 support (model and dataset upload)
6. NeptuneAI logger support (metric, model and dataset logging)
7. Classwise AP logging during experiments

Install

Install yolov5 using pip (for Python >=3.7)

pip install yolov5

Model Zoo

Effortlessly explore and use finetuned YOLOv5 models with one line of code: awesome-yolov5-models

Use from Python

import yolov5

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

# or load custom model
model = yolov5.load('train/best.pt')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# 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, y1, x2, 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/')

Train/Detect/Test/Export

• You can directly use these functions by importing them:

from yolov5 import train, val, detect, export
# from yolov5.classify import train, val, predict
# from yolov5.segment import train, val, predict

train.run(imgsz=640, data='coco128.yaml')
val.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:

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)

Use from CLI

You can call yolov5 train, yolov5 detect, yolov5 val and yolov5 export commands after installing the package via pip:

Training

• Finetune one of the pretrained YOLOv5 models using your custom data.yaml:

$ yolov5 train --data data.yaml --weights yolov5s.pt --batch-size 16 --img 640
                                          yolov5m.pt              8
                                          yolov5l.pt              4
                                          yolov5x.pt              2

• Start a training using a COCO formatted dataset:

# data.yml
train_json_path: "train.json"
train_image_dir: "train_image_dir/"
val_json_path: "val.json"
val_image_dir: "val_image_dir/"

$ yolov5 train --data data.yaml --weights yolov5s.pt

• Train your model using Roboflow Universe datasets (roboflow>=0.2.29 required):

$ yolov5 train --data DATASET_UNIVERSE_URL --weights yolov5s.pt --roboflow_token YOUR_ROBOFLOW_TOKEN

Where DATASET_UNIVERSE_URL must be in https://universe.roboflow.com/workspace_name/project_name/project_version format.

• Visualize your experiments via Neptune.AI (neptune-client>=0.10.10 required):

$ yolov5 train --data data.yaml --weights yolov5s.pt --neptune_project NAMESPACE/PROJECT_NAME --neptune_token YOUR_NEPTUNE_TOKEN

• Automatically upload weights to Huggingface Hub:

$ yolov5 train --data data.yaml --weights yolov5s.pt --hf_model_id username/modelname --hf_token YOUR-HF-WRITE-TOKEN

• Automatically upload weights and datasets to AWS S3 (with Neptune.AI artifact tracking integration):

export AWS_ACCESS_KEY_ID=YOUR_KEY
export AWS_SECRET_ACCESS_KEY=YOUR_KEY

$ yolov5 train --data data.yaml --weights yolov5s.pt --s3_upload_dir YOUR_S3_FOLDER_DIRECTORY --upload_dataset

• Add yolo_s3_data_dir into data.yaml to match Neptune dataset with a present dataset in S3.

# data.yml
train_json_path: "train.json"
train_image_dir: "train_image_dir/"
val_json_path: "val.json"
val_image_dir: "val_image_dir/"
yolo_s3_data_dir: s3://bucket_name/data_dir/

Inference

yolov5 detect command runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ yolov5 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

Export

You can export your fine-tuned YOLOv5 weights to any format such as torchscript, onnx, coreml, pb, tflite, tfjs:

$ yolov5 export --weights yolov5s.pt --include torchscript,onnx,coreml,pb,tfjs

Classify

Train/Val/Predict with YOLOv5 image classifier:

$ yolov5 classify train --img 640 --data mnist2560 --weights yolov5s-cls.pt --epochs 1

$ yolov5 classify predict --img 640 --weights yolov5s-cls.pt --source images/

Segment

Train/Val/Predict with YOLOv5 instance segmentation model:

$ yolov5 segment train --img 640 --weights yolov5s-seg.pt --epochs 1

$ yolov5 segment predict --img 640 --weights yolov5s-seg.pt --source images/