https://github.com/iboud0/lra-image-classification-on-sequences-of-pixels
Benchmarking sequence-based models on CIFAR-10 for the LRA benchmark. Implemented and evaluated models like TrellisNet, LipschitzRNN, PiT, Nyströmformer, and FNet for image classification
https://github.com/iboud0/lra-image-classification-on-sequences-of-pixels
benchmarking cifar10 deep-learning image-classification long-range-arena machine-learning neural-networks python research sequence-modeling transformers
Last synced: 3 months ago
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Benchmarking sequence-based models on CIFAR-10 for the LRA benchmark. Implemented and evaluated models like TrellisNet, LipschitzRNN, PiT, Nyströmformer, and FNet for image classification
- Host: GitHub
- URL: https://github.com/iboud0/lra-image-classification-on-sequences-of-pixels
- Owner: iboud0
- Created: 2024-12-09T20:29:49.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-10T08:17:21.000Z (6 months ago)
- Last Synced: 2025-01-08T10:18:36.533Z (5 months ago)
- Topics: benchmarking, cifar10, deep-learning, image-classification, long-range-arena, machine-learning, neural-networks, python, research, sequence-modeling, transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 192 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# LRA Image Classification on Sequences of Pixels
## Overview
This repository showcases the implementation and benchmarking of various machine learning models for **image classification** on the **CIFAR-10 dataset**. The focus of this project is inspired by the **Long Range Arena (LRA)** benchmark, where images are processed as sequences of pixels rather than as grids.
The primary goal is to evaluate how well sequence-processing models can handle image-based tasks, and to explore their potential advantages and limitations compared to traditional image-processing architectures.
---
## Motivation
Traditional image classification models rely on convolutional neural networks (CNNs), which exploit the spatial structure of images. In this project, we explore **sequence-based approaches**:
- Treating each pixel as part of a sequence.
- Evaluating the capability of sequence models to handle image data.
The **CIFAR-10 dataset**:
- **60,000 images**, each of size 32x32 pixels with 3 color channels (RGB).
- Divided into **10 classes**, with 6,000 images per class.
This task aligns with the LRA benchmark's objective of testing models on long-range dependency tasks.
---
## Included Models
The following sequence-based models were benchmarked and some were implemented from scratch:
1. **TrellisNet**:
- Sequence modeling architecture combining features from recurrent and convolutional networks.
- Key Features:
- Weight tying across time and layers.
- Uses temporal convolutions for hierarchical feature learning.
- Performance:
- **Paper Accuracy**: 73.42%
- **Our Work**: 37%
2. **LipschitzRNN**:
- Enforces Lipschitz constraints for stability in training.
- Performance:
- **Paper Accuracy**: 64.2%
- **Our Work**: 40.5%
3. **Pixel Transformer (PiT)**:
- Treats each pixel as a token, avoiding locality inductive biases.
- Architecture:
- Linear projection of RGB values.
- Transformer Encoder with Multihead Self-Attention.
- Performance:
- **Paper Accuracy**: 85.1% (on CIFAR-100)
- **Our Work**: 42.16%
4. **Nyströmformer**:
- Transformer variant that approximates self-attention using the Nyström method.
- Performance:
- **Paper Accuracy**: 41.58%
- **Our Work**: 23.09% (15.05% in individual implementation)
5. **FNet**:
- Replaces self-attention with Fourier Transform for faster token mixing.
- Performance:
- **Paper Accuracy**: Not Available
- **Our Work**: 46.02%
---
## Repository Structure
Here is an overview of the repository:
Each model directory contains:
- **Implementation scripts**: For training and evaluation.
- **Preprocessing details**: Steps for preparing the CIFAR-10 dataset.
- **Guidelines**: Specific instructions to reproduce results.
---
## How to Use
### Installation
1. Clone the repository:
```bash
git clone https://github.com/iboud/LRA-Image-Classification-on-Sequences-of-Pixels.git
cd LRA-Image-Classification-on-Sequences-of-Pixels
```
Note: No required dependencies.
### Running Models
Each model folder contains details explaining:
- Dataset preparation.
- Training and evaluation steps.
- Hyperparameter configurations.
---
## Results and Benchmarking
The following table summarizes the experimental results for each model:
| Model | Paper Accuracy | Our Accuracy |
|----------------|----------------|--------------|
| TrellisNet | 73.42% | 37% |
| LipschitzRNN | 64.2% | 40.5% |
| PiT | 85.1% (C100) | 42.16% |
| Nyströmformer | 41.58% | 23.09% |
| FNet | NA | 46.02% |
---
## Challenges and Limitations
1. **Resource Constraints**:
- Limited GPU memory restricted model size and batch processing.
- High computational demands, especially for models like PiT and Nyströmformer.
2. **Accuracy Gaps**:
- Some models showed significant differences from reported paper accuracies, likely due to hardware and training duration limitations.
3. **Dataset Representation**:
- Processing CIFAR-10 as pixel sequences increases sequence lengths, adding complexity for transformer-based models.
---
## Learnings and Takeaways
- Sequence models can handle image classification but face challenges with long sequence lengths (e.g., **PiT**, **Nyströmformer**).
- Trade-offs exist between computational efficiency and accuracy for different architectures.
- Fine-tuning hyperparameters and leveraging larger datasets could further improve results.
---
## References
1. [TrellisNet Paper](https://arxiv.org/pdf/1810.06682v2)
2. [LipschitzRNN Paper](https://arxiv.org/pdf/2006.12070v3)
3. [PiT Paper](https://arxiv.org/abs/2406.09415)
4. [Nyströmformer Paper](https://arxiv.org/pdf/2102.03902)
5. [FNet Paper](https://arxiv.org/pdf/2105.03824)
6. [LRA Benchmark Paper](https://arxiv.org/abs/2011.04006)
---
## Contributors
This project was carried out by:
- **Imane Rahali**
- **Amine Idrissi**
- **Ilyas Boudhaine**
Supervised by:
- **Hamza Alami**
- **Issam Ait Yahia**
---