readme: ---
  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}
  }
  ```