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---
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:
-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)
```
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