Object Detection Inference Interface (ODII)

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.

✨ Features

• 🚀 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.

📦 Supported 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

📦 Reference for COCO Pretrained Weights

• YOLOX
• YOLOv3
• YOLOv4
• YOLOv6
• YOLOv7

🛠️ Requirements

• Python >= 3.8
• pip >= 24.2

📥 Installation

1. 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

2. 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 .

🛠️ Usage

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

📊 Results Format

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]
}

🙏 Acknowledgements

1. https://github.com/Megvii-BaseDetection/YOLOX
2. https://github.com/Tianxiaomo/pytorch-YOLOv4
3. https://github.com/meituan/YOLOv6
4. https://github.com/WongKinYiu/yolov7
5. https://github.com/eriklindernoren/PyTorch-YOLOv3