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Add yolov5 model card

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+
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+ ---
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+ tags:
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+ - yolov5
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+ - yolo
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+ - vision
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+ - object-detection
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+ - pytorch
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+ library_name: yolov5
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+ library_version: 7.0.6
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+ inference: false
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+
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+ datasets:
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+ - keremberke/csgo-object-detection
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+
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+ model-index:
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+ - name: keremberke/yolov5n-csgo
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+ results:
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+ - task:
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+ type: object-detection
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+
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+ dataset:
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+ type: keremberke/csgo-object-detection
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+ name: keremberke/csgo-object-detection
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+ split: validation
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+
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+ metrics:
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+ - type: precision # since mAP@0.5 is not available on hf.co/metrics
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+ value: 0.9081207114929885 # min: 0.0 - max: 1.0
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+ name: mAP@0.5
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+ ---
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+
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+ <div align="center">
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+ <img width="640" alt="keremberke/yolov5n-csgo" src="https://huggingface.co/keremberke/yolov5n-csgo/resolve/main/sample_visuals.jpg">
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+ </div>
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+
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+ ### How to use
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+
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+ - Install [yolov5](https://github.com/fcakyon/yolov5-pip):
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+
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+ ```bash
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+ pip install -U yolov5
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+ ```
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+
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+ - Load model and perform prediction:
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+
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+ ```python
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+ import yolov5
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+
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+ # load model
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+ model = yolov5.load('keremberke/yolov5n-csgo')
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+
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+ # set model parameters
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+ model.conf = 0.25 # NMS confidence threshold
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+ model.iou = 0.45 # NMS IoU threshold
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+ model.agnostic = False # NMS class-agnostic
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+ model.multi_label = False # NMS multiple labels per box
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+ model.max_det = 1000 # maximum number of detections per image
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+
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+ # set image
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+ img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
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+
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+ # perform inference
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+ results = model(img, size=640)
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+
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+ # inference with test time augmentation
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+ results = model(img, augment=True)
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+
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+ # parse results
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+ predictions = results.pred[0]
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+ boxes = predictions[:, :4] # x1, y1, x2, y2
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+ scores = predictions[:, 4]
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+ categories = predictions[:, 5]
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+
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+ # show detection bounding boxes on image
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+ results.show()
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+
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+ # save results into "results/" folder
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+ results.save(save_dir='results/')
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+ ```
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+
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+ - Finetune the model on your custom dataset:
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+
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+ ```bash
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+ yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-csgo --epochs 10
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+ ```
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+