Update README.md

README.md

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 ---
 license: apache-2.0
+pipeline_tag: object-detection
 ---
+# 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
+import torch
+import numpy as np
+from models.common import DetectMultiBackend
+from utils.general import non_max_suppression, scale_boxes
+from utils.torch_utils import select_device, smart_inference_mode
+from utils.augmentations import letterbox
+import PIL.Image
+
+@smart_inference_mode()
+def predict(image_path, weights='yolov9-c.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45):
+    # Initialize
+    device = select_device('0')
+    model = DetectMultiBackend(weights='yolov9-c.pt', device="0", fp16=False, data='data/coco.yaml')
+    stride, names, pt = model.stride, model.names, model.pt
+
+    # Load image
+    image = np.array(PIL.Image.open(image_path))
+    img = letterbox(img0, imgsz, stride=stride, auto=True)[0]
+    img = img[:, :, ::-1].transpose(2, 0, 1)
+    img = np.ascontiguousarray(img)
+    img = torch.from_numpy(img).to(device).float()
+    img /= 255.0
+    if img.ndimension() == 3:
+        img = img.unsqueeze(0)
+
+    # Inference
+    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). 🧡