This repository contains the code and supplementary materials for our paper titled "FusionNet: Leveraging Advanced Feature Fusion for Efficient Object Detection", which has been accepted at ICCV 2023 (International Conference on Computer Vision).
In this paper, we propose a novel deep learning approach for object detection, which aims to identify and localize objects of interest in an image. Our method leverages the power of convolutional neural networks (CNNs) and advanced feature fusion techniques to achieve highly accurate and efficient object detection results. We demonstrate the effectiveness of our approach on various challenging datasets, showcasing its potential for real-world applications.
To install and set up the required dependencies, follow these steps:
- Clone this repository:
git clone https://github.com/daidshow/FusionNet.git
- Install the necessary packages:
pip install -r requirements.txt
- Download the pretrained models: Download Link
- Unzip the pretrained models and place them in the
models/
directory.
To train and evaluate the object detection model, follow these instructions:
- Prepare your dataset by following the guidelines in
data_preparation.md
. - Run the training script:
python train.py --dataset /path/to/dataset --epochs 50
- Evaluate the trained model:
python evaluate.py --dataset /path/to/dataset --model /path/to/model.pth
- Perform object detection on new images:
python detect.py --image /path/to/image.jpg --model /path/to/model.pth
For more detailed usage instructions and options, please refer to the documentation.
We evaluate our approach on the following datasets:
Please download the datasets and follow the instructions in data_preparation.md
to preprocess the data.
We provide pretrained models for our approach, which can be downloaded from the following links:
To use the pretrained models, simply load them using torch.load()
in your own code.
If you find our work helpful in your research, please consider citing our paper: