metadata

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.

from huggingface_hub import hf_hub_download 
hf_hub_download("merve/yolov9", filename="yolov9-c.pt", local_dir="./")

Load the model.

# 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. 🧡