Update README.md

README.md

@@ -1,3 +1,57 @@
----
-license: mit
----
+---
+license: gpl-3.0
+inference: false
+tags:
+  - instance-segmentation
+  - computer-vision
+  - vision
+  - yolo
+  - yolov8
+datasets:
+- detection-datasets/coco
+---
+### How to use
+
+- Install yolov8:
+
+```bash
+pip install -U yolov8
+```
+
+- Load model and perform prediction:
+
+```python
+import yolov5
+# load model
+model = yolov5.load('fcakyon/yolov5n-v7.0')
+  
+# 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)
+# inference with larger input size
+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 --img 640 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 10 --device cuda:0
+```