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---
license: gpl-3.0
tags:
- object-detection
- computer-vision
- yolov7
- pypi
datasets:
- detection-datasets/coco
---
### Model Description
[YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
[YOLOv7-Pip: Packaged version of the Yolov7 repository](https://github.com/kadirnar/yolov7-pip)
[Paper Repo: Implementation of paper - YOLOv7](https://github.com/WongKinYiu/yolov7)
### Installation
```
pip install yolov7detect
```
### Yolov7 Inference
```python
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}
}
``` |