ODII is a Python package designed to provide a unified and streamlined interface for running inference on multiple object detection models under one hood. ODII facilitates seamless interaction with a range of popular models, including YOLOX, YOLOv3, YOLOv4, YOLOv6, and YOLOv7, without the need to manage multiple codebases or installation processes.
- 🚀 Unified Interface: Interact with multiple object detection models using a single, easy-to-use interface.
- 🧹 Reduced Boilerplate: Simplifies the setup process by handling the installation of multiple models with varying instructions.
- 📚 Lower Learning Curve: Minimizes the complexity of understanding and writing inference code, making it easier to work with different models.
- 🔄 Extensibility: Easily extend the interface to support additional object detection models.
- YOLOX : https://github.com/Megvii-BaseDetection/YOLOX
- YOLOv3 : https://github.com/eriklindernoren/PyTorch-YOLOv3
- YOLOv4 : https://github.com/Tianxiaomo/pytorch-YOLOv4
- YOLOv6 : https://github.com/meituan/YOLOv6
- YOLOv7 : https://github.com/WongKinYiu/yolov7
- Python >= 3.8
- pip >= 24.2
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Install PyTorch: Follow the instructions on the PyTorch website to install the appropriate version of PyTorch for your system.
For example, using pip:
pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
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Clone the Repository and Install Dependencies:
git clone https://github.com/GirinChutia/Object-Detection-Inference-Interface.git cd Object-Detection-Inference-Interface python -m pip install -e .
Here is an example of how to use ODII to run inference on an image:
from odii import INFERENCE, plot_results, load_classes, load_yaml # Load the classnames classnames = load_classes('coco.names') # ['person','bicycle','car', ... ] # Set the model paths & configs # (COCO Pretrained weights can be downloaded from links provided in "Reference for COCO Pretrained Weights" section) model_config = {'yolov7': {'weights': 'weights/yolov7/yolov7.pt', 'config': None}, 'yolov4': {'weights': 'weights/yolov4/yolov4.weights', 'config': 'weights/yolov4/yolov4.cfg'},} # Set Device device = 'cuda' # Input image path image_path = 'tests/images/test_image.jpg' # --- Infer yolov7 model --- model_name = 'yolov7' INF = INFERENCE(model_name=model_name, device=device, model_paths={'weights': model_config[model_name]['weights'], 'config': model_config[model_name]['config']}) yolov7_result = INF.infer_image(image_path=image_path, confidence_threshold=0.4, nms_threshold=0.4) # --- Infer yolov4 model --- model_name = 'yolov4' INF = INFERENCE(model_name=model_name, device=device, model_paths={'weights': model_config[model_name]['weights'], 'config': model_config[model_name]['config']}) yoloxm_result = INF.infer_image(image_path=image_path, confidence_threshold=0.4, nms_threshold=0.4)More details for inference can be found in this notebook : inference_demo.ipynb
The inference results are returned as a dictionary with the following format:
{ 'boxes': [ [74, 11, 900, 613], [77, 149, 245, 361], [560, 359, 737, 565], [139, 38, 414, 610] ], 'scores': [ 0.8257260322570801, 0.8446129560470581, 0.8616959452629089, 0.9366706013679504 ], 'classes': [2, 16, 28, 0] }