Update README.md

README.md

@@ -1,87 +1,21 @@
 ---
-license: afl-3.0
----
----
+description: Ultralytics best model trained on dataset.yaml
+author: Ultralytics
+version: 8.0.116
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: player
+  1: football
+license: gpl-3.0
 tags:
-- yolov5
-- yolo
-- vision
-- object-detection
-- pytorch
-library_name: yolov5
-library_version: 7.0.6
-inference: false
-
+  - object-detection
+  - computer-vision
+  - yolov8
 datasets:
-- keremberke/license-plate-object-detection
-
-model-index:
-- name: keremberke/yolov5m-license-plate
-  results:
-  - task:
-      type: object-detection
-
-    dataset:
-      type: keremberke/license-plate-object-detection
-      name: keremberke/license-plate-object-detection
-      split: validation
-
-    metrics:
-      - type: precision  # since [email protected] is not available on hf.co/metrics
-        value: 0.9882982754936463  # min: 0.0 - max: 1.0
-        name: [email protected]
+  - detection-datasets/coco
 ---
-
-<div align="center">
-  <img width="640" alt="keremberke/yolov5m-license-plate" src="https://huggingface.co/keremberke/yolov5m-license-plate/resolve/main/sample_visuals.jpg">
-</div>
-
-
-```bash
-pip install -U yolov5
-```
-
-- Load model and perform prediction:
-
-```python
-import yolov5
-
-# load model
-model = yolov5.load('keremberke/yolov5m-license-plate')
-  
-# 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-license-plate --epochs 10
-```
-
-**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)*