Update README.md

README.md

@@ -1,35 +1,14 @@
 ---
 pipeline_tag: object-detection
+license: apache-2.0
+metrics:
+- accuracy
 ---
-# YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
-
-This is the model repository for YOLOv9, containing the following checkpoints:
-
-- GELAN-C (a newer, lighter architecture)
-- GELAN-E
-- YOLO9-C
-- YOLO9-E
-
-### How to Use
-
-Clone YOLOv9 repository.
-
-```
 git clone https://github.com/WongKinYiu/yolov9.git
 cd yolov9
-```
-
-Download the weights using `hf_hub_download` and use the loading function in helpers of YOLOv9.
-
-```python
 from huggingface_hub import hf_hub_download 
 hf_hub_download("merve/yolov9", filename="yolov9-c.pt", local_dir="./")
-```
-
-Load the model.
-
-```python
-# make sure you have the following dependencies
+# Make sure you have the necessary dependencies installed
 import torch
 import numpy as np
 from models.common import DetectMultiBackend
@@ -59,18 +38,4 @@ def predict(image_path, weights='yolov9-c.pt', imgsz=640, conf_thres=0.1, iou_th
     pred = model(img, augment=False, visualize=False)
 
     # Apply NMS
-    pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000)
-```
-
-### Citation
-
-```
-@article{wang2024yolov9,
-  title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
-  author={Wang, Chien-Yao  and Liao, Hong-Yuan Mark},
-  booktitle={arXiv preprint arXiv:2402.13616},
-  year={2024}
-}
-```
-
-The Colab notebook can be found [here](https://colab.research.google.com/drive/1U3rbOmAZOwPUekcvpQS4GGVJQYR7VaQX?usp=sharing#scrollTo=k-JxtpQ_2e0F). 🧡
+    pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000)