https://github.com/eidoslab/neuralvelocity
NeVe - Neural Velocity for hyperparameter tuning (IJCNN 2025)
https://github.com/eidoslab/neuralvelocity
Last synced: 7 months ago
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NeVe - Neural Velocity for hyperparameter tuning (IJCNN 2025)
- Host: GitHub
- URL: https://github.com/eidoslab/neuralvelocity
- Owner: EIDOSLAB
- License: gpl-3.0
- Created: 2025-04-04T14:34:10.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2025-06-17T16:48:34.000Z (7 months ago)
- Last Synced: 2025-06-17T17:43:48.158Z (7 months ago)
- Language: Python
- Size: 1.08 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧠 [IJCNN 2025] NeVe: Neural Velocity for hyperparameter tuning
[](https://www.docker.com/)
[](https://developer.nvidia.com/cuda-zone)
[](https://www.python.org/downloads/release/python-388/)
[](https://pytorch.org/)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://arxiv.org/abs/xxxx.xxxxx)
This repository contains the official implementation of the paper:
> **Neural Velocity for hyperparameter tuning**
> *Gianluca Dalmasso, et al.*
> IJCNN 2025
> 📄 [arXiv / DOI link here]
---
## 📂 Project Structure
```bash
NeuralVelocity/
├── 📁 assets/ # Teaser images, figures, etc.
├── 📁 src/
│ ├── 📁 dataloaders/ # CIFAR, ImageNet loaders
│ ├── 📁 labelwave/ # Competing method: LabelWave
│ ├── 📁 ultimate_optimizer/ # Competing method: Ultimate Optimizer
│ ├── 📁 neve/ # 💡 Core method: Neural Velocity
│ ├── 📁 models/ # Model architectures (e.g. CIFAR ResNets, INet ResNets, ...)
│ ├── 📁 optimizers/ # Optimizers
│ ├── 📁 schedulers/ # LR schedulers
│ ├── 📁 swin_transformer/ # Swin Transformer model architecture
│ ├── arguments.py # CLI args and config parser
│ ├── classification.py # Training pipeline (base)
│ ├── classification_labelwave.py # For LabelWave experiments
│ ├── classification_ultimate_optimizer.py # For Ultimate Optimizer experiments
│ └── utils.py # Utility functions
├── Dockerfile # Default Docker container
├── Dockerfile.python # Base Python environment
├── Dockerfile.sweep # Sweep setup (e.g. for tuning)
├── LICENSE # GNU GPLv3 license
├── README.md # Project overview
├── build.sh # Build script (e.g. for Docker or sweep)
├── requirements.txt # Python dependencies
└── setup.py # Install package for pip
```
---
## 🚀 Getting Started
You can run this project either using a Python virtual environment or a Docker container.
#### ✅ Clone the repository
```bash
git clone https://github.com/EIDOSLAB/NeuralVelocity.git
cd NeuralVelocity
```
### 🧪 Option A — Run with virtual environment (recommended for development)
#### 📦 Create virtual environment & install dependencies
> This project was developed and tested with Python 3.8.8 — we recommend using the same version for full compatibility and reproducibility.
```bash
# 1. Install Python 3.8.8 (only once)
pyenv install 3.8.8
# 2. Create virtual environment
pyenv virtualenv 3.8.8 neve
# 3. Activate the environment
pyenv activate neve
# 4. Install dependencies
pip install -r requirements.txt
```
#### 🚀 Run training
```bash
cd src
python classification.py
```
### 🐳 Option B — Run with Docker
You can also use Docker for full environment reproducibility.
#### 🏗️ Build Docker images and push to remote registry
The `build.sh` script automates the build of all Docker images and pushes them to the configured remote Docker registry.
Before running, make sure to edit `build.sh` to set your remote registry URL and credentials if needed.
Run:
```bash
bash build.sh
```
This will build the following Docker images:
- `neve:base` (default container for training and experiments)
- `neve:python` (base Python environment)
- `neve:sweep` (for hyperparameter sweep experiments)
#### 🚀 Run training inside the container
```bash
docker run --rm -it \
--gpus all \ # Optional: remove if no GPU
neve:python classification.py # Optional: Optional parameters...
```
> 💡 Note: you may need to adjust volume mounting (-v) depending on your OS and Docker setup.
---
## 📊 Datasets
Tested datasets:
- [CIFAR10, and CIFAR100](https://www.cs.toronto.edu/~kriz/cifar.html)
- [Imagenet-100](https://www.image-net.org/challenges/LSVRC/2012/) (must be downloaded separately and prepared in the standard folder format.)
---
## 🪪 License
This project is licensed under the **GNU General Public License v3.0**.
See the [LICENSE](./LICENSE) file for details.
➡️ You are free to use, modify, and distribute this code under the same license terms.
Any derivative work must also be distributed under the GNU GPL.
---
## 🙌 Acknowledgments
This research was developed at the University of Turin (UniTO), within the [EIDOS Lab](https://www.di.unito.it/~eidos/), and Télécom Paris.
We thank the members of both institutions for the insightful discussions and support during the development of this work.
---
## 📜 Citation
If you use this repository or find our work helpful, please cite:
```bibtex
@misc{dalmasso2025neve,
title = {Neural Velocity for Hyperparameter Tuning},
author = {Gianluca Dalmasso and Others},
year = {2025},
howpublished = {\url{https://arxiv.org/abs/xxxx.xxxxx}},
note = {Accepted at IJCNN 2025. Official citation will be updated upon publication.}
}
```
---
## 📫 Contact
For questions or collaborations, feel free to reach out:
- 📧 gianluca.dalmasso@unito.it
- 🐙 GitHub Issues for bugs or feature requests