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Model Description

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv7-Pip: Packaged version of the Yolov7 repository

Paper Repo: Implementation of paper - YOLOv7

Installation

pip install yolov7detect

Yolov7 Inference

import yolov7

# load pretrained or custom model
model = yolov7.load('kadirnar/yolov7-tiny-v0.1', hf_model=True)

# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.classes = None  # (optional list) filter by class

# set image
imgs = 'inference/images'

# perform inference
results = model(imgs)

# inference with larger input size and test time augmentation
results = model(img, size=1280, 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()

BibTeX Entry and Citation Info

@article{wang2022yolov7,
 title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
 author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
 journal={arXiv preprint arXiv:2207.02696},
 year={2022}
}
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