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ConvMixer Implementation

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This repository provides an implementation of ConvMixer, a convolutional neural network architecture for image recognition tasks. It is based on the paper "Patches Are All You Need? 🤷" and offers pre-trained model weights for evaluation and code for training on datasets like ImageNet-1k and CIFAR-10.

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Description

This GitHub repository, locuslab/convmixer, hosts the implementation of ConvMixer, a neural network architecture detailed in the ICLR 2022 paper titled "Patches Are All You Need? 🤷" by Asher Trockman and Zico Kolter. The primary code for the ConvMixer model is located in `convmixer.py`. The project leverages the `pytorch-image-models` (timm) framework for training and includes modifications to this framework to support ConvMixers.

Key modifications within the `pytorch-image-models` framework include the addition of `timm/models/convmixer.py`, updates to `timm/models/__init__.py`, and the integration of a "OneCycle" learning rate schedule with corresponding scheduler files. While the OneCycle schedule is noted, the authors suggest that standard cosine schedules could also be effective for training ConvMixers.

The repository offers pre-trained model weights for several ConvMixer variants, including ConvMixer-1536/20, ConvMixer-768/32, and ConvMixer-1024/20, with details on their kernel size, patch size, and file size. Instructions and example commands are provided for evaluating these models on datasets like ImageNet-1k, aiming for high accuracy. For instance, ConvMixer-1536/20 is expected to achieve 81.37% accuracy on ImageNet-1k validation.

Training ConvMixers is also detailed, with an example command for distributed training on a multi-GPU setup using ImageNet-1k. The training command specifies hyperparameters such as batch size, number of workers, optimizer, epochs, learning rate schedule, augmentation techniques (e.g., RandAugment, CutMix, MixUp), and input size. The repository also includes a simplified, tweetable version of the ConvMixer architecture in Python.

Additionally, a separate repository for training ConvMixers on CIFAR-10 is linked, and advice is given on adjusting training parameters like `--scale` for smaller input sizes. The project is open-source under the MIT license, encouraging community contribution and further development.

ConvMixer Implementation Highlights

  • Implementation of ConvMixer neural network architecture

  • Based on the research paper "Patches Are All You Need? 🤷"

  • Includes code for training ConvMixers

  • Provides pre-trained model weights for evaluation

  • Supports training on ImageNet-1k and CIFAR-10 datasets

  • Leverages the `pytorch-image-models` (timm) framework

  • Includes a "OneCycle" learning rate scheduler implementation

  • Offers example commands for model evaluation

  • Includes example commands for distributed training

  • Contains a simplified, tweetable version of the ConvMixer model

  • Model weights available for download

  • Open-source under the MIT license

Getting Started with ConvMixer Implementation

  1. Access Model: Clone the GitHub repository to your local machine.

  2. Set Up Environment: Install necessary Python libraries, including PyTorch and `pytorch-image-models`.

  3. Load Model: Instantiate a ConvMixer model using the provided Python scripts.

  4. Evaluate Model: Use the `validate.py` script with downloaded weights and a validation dataset.

  5. Train Model: Execute the `distributed_train.sh` script with specified dataset paths and training parameters.

  6. Integrate: Adapt the `convmixer.py` code for custom training loops or inference pipelines.

ConvMixer Implementation's Use Cases

  • Image Classification
  • Model Evaluation
  • Custom Model Training
  • Research and Development
  • Transfer Learning
  • Benchmarking

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