Add yolov5 model card

README.md

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+ 
+---
+tags:
+- yolov5
+- yolo
+- vision
+- object-detection
+- pytorch
+library_name: yolov5
+library_version: 7.0.6
+inference: false
+
+datasets:
+- keremberke/nfl-object-detection
+
+model-index:
+- name: keremberke/yolov5m-nfl
+  results:
+  - task:
+      type: object-detection
+
+    dataset:
+      type: keremberke/nfl-object-detection
+      name: keremberke/nfl-object-detection
+      split: validation
+
+    metrics:
+      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
+        value: 0.3141797014905773  # min: 0.0 - max: 1.0
+        name: mAP@0.5
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/yolov5m-nfl" src="https://huggingface.co/keremberke/yolov5m-nfl/resolve/main/sample_visuals.jpg">
+</div>
+
+### How to use
+
+- Install [yolov5](https://github.com/fcakyon/yolov5-pip):
+
+```bash
+pip install -U yolov5
+```
+
+- Load model and perform prediction:
+
+```python
+import yolov5
+
+# load model
+model = yolov5.load('keremberke/yolov5m-nfl')
+  
+# 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, size=640)
+
+# 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/')
+```
+
+- Finetune the model on your custom dataset:
+
+```bash
+yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-nfl --epochs 10
+```
+