Update README.md

README.md

@@ -1,3 +1,68 @@
 ---
 license: mit
+datasets:
+- garythung/trashnet
+- Zesky665/TACO
+- detection-datasets/coco
+language:
+- en
+tags:
+- object-detection
+- computer-vision
+- yolov5
 ---
+
+<div align="center">
+  <img width="416" alt="turhancan97/yolov5-detect-trash-classification" src="https://huggingface.co/turhancan97/yolov5-detect-trash-classification/resolve/main/example1.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('turhancan97/yolov5-detect-trash-classification')
+  
+# 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 416 --batch 16 --weights turhancan97/yolov5-detect-trash-classification --epochs 10
+```