Update README.md

README.md

@@ -1,9 +1,54 @@
+
 ---
-license: apache-2.0
-language:
-- en
 pipeline_tag: object-detection
-tags:
-- void
-- yolov9
----
+---
+# YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
+
+This is the model repository for YOLOv9, containing the following checkpoints:
+
+-best.pt
+
+
+
+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("SakshiRathi77/void-150-epoch", filename="best.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='best.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45):
+    # Initialize
+    device = select_device('0')
+    model = DetectMultiBackend(weights='best.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)
+```