Update README.md

README.md

@@ -1,9 +1,47 @@
 ---
+license: agpl-3.0
+pipeline_tag: object-detection
 tags:
+- ultralytics
+- yolo
+- yolov8
+- tracking
+- instance-segmentation
+- image-classification
+- pose-estimation
+- obb
+- object-detection
+- yolov3
+- yolov5
 - pytorch_model_hub_mixin
 - model_hub_mixin
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
+
+## Installation
+
+First install the package:
+
+```bash
+!pip install -q git+https://github.com/nielsrogge/ultralytics.git@feature/add_hf
+```
+
+## Usage
+
+YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO.from_pretrained("nielsr/yolov8n")
+
+# Use the model
+model.train(data="coco128.yaml", epochs=3)  # train the model
+metrics = model.val()  # evaluate model performance on the validation set
+results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
+path = model.export(format="onnx")  # export the model to ONNX format
+```
+
+See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.