# dhd

**Repository Path**: bellaso/dhd

## Basic Information

- **Project Name**: dhd
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-30
- **Last Updated**: 2025-12-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond

Welcome to the official PyTorch implementation of **"Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond."** We have open-sourced this repository to foster research and collaboration in the field of multi-drone trajectory prediction and related areas.

## Code Availability

The implementation code is now available.

### Compatibility
This implementation is compatible with PyTorch 2.x, and has been verified to run on NVIDIA H100 GPUs for both training and inference. No additional changes are required—just ensure your CUDA and driver versions support H100, and install the necessary dependencies from environment-torch-2.0.yml.


### Latest News
**"Drones Help Drones"** has been accepted as a Poster at the **Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024).** You can access the paper on [arXiv](https://arxiv.org/abs/2405.14674).

## Setup Instructions

### Step 1: Create the Conda Environment

To set up the environment, use the following command:

```bash
conda env create -f environment.yml
```
### Step 2: Replace `splits.py`

Ensure you replace the `splits.py` file in the `nuscenes` package (typically found at `/miniconda3/envs/dhd/lib/python3.7/site-packages/nuscenes/utils/splits.py`) with our provided version of `splits.py`.

### Step 3: Download the Dataset

Download the complete **Air-Co-Pred** dataset, which includes the Trainval dataset (metadata and file blobs parts 0-36), from the following link:

[Download Link] [https://pan.baidu.com/s/1XRgtXcLHS4fk02EqE-mYUQ](https://pan.baidu.com/s/1xfO2IxsWisg6EHzCQlka0A )
Access Code: `u4ko`
or https://huggingface.co/datasets/BRUCE-WZC/AIR
Once downloaded, extract the `.tar` files into your desired data root directory (`YOUR_DATAROOT`), organizing them as follows:

```bash
Air-Co-Pred/
├── trainval/
│   ├── maps/
│   ├── samples/
│   ├── sweeps/
│   └── v1.0-trainval/
```

## Model Training

To train the DHD (Drones Help Drones) model, execute the following command:

```bash
python train.py --config=dhd/config/dhd.yml \
                LOG_DIR xxx \
                GPUS [x,x,x,x] \
                BATCHSIZE 1 \
                DATASET.DATAROOT YOUR_DATAROOT
```

## Model Evaluation

To evaluate the model with trained weights, run:

```bash
python test.py --config dhd/config/dhd.yml \
                PRETRAINED.LOAD_WEIGHTS True \
                PRETRAINED.PATH $YOUR_PRETRAINED_WEIGHTS_PATH \
                GPUS [x,x,x,x] \
                BATCHSIZE 1 \
                DATASET.DATAROOT YOUR_DATAROOT
```

## Citation

If you find this work helpful in your research, please consider citing us:

```bibtex
@inproceedings{
  title={Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond},
  author={Wang Z, Cheng P, Chen M, Tian P, Wang Z, Li X, Yang X, Sun X.},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024}
}
```

```bibtex
@misc{wang2024droneshelpdronescollaborative,
      title={Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond}, 
      author={Zhechao Wang and Peirui Cheng and Mingxin Chen and Pengju Tian and Zhirui Wang and Xinming Li and Xue Yang and Xian Sun},
      year={2024},
      eprint={2405.14674},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2405.14674}, 
}
```