ConvMixer 768/32
| Property | Value |
|---|---|
| Parameter Count | 21.2M |
| License | MIT |
| Paper | Patches Are All You Need? |
| Image Size | 224 x 224 |
| GMACs | 19.5 |
What is convmixer_768_32.in1k?
ConvMixer 768/32 is an innovative vision model that challenges the notion that attention mechanisms are necessary for modern computer vision tasks. It's a purely convolutional architecture that achieves competitive performance on ImageNet-1k classification while maintaining architectural simplicity.
Implementation Details
The model implements a patch-based approach with 768 channels and 32 layers, utilizing standard convolutions instead of self-attention mechanisms. It processes 224x224 pixel images and requires 19.5 GMACs (Giga Multiply-Accumulate Operations) for inference.
- Efficient feature extraction with 21.1M parameters
- Activation size of 26.0M
- Supports both classification and embedding generation
Core Capabilities
- Image classification on ImageNet-1k dataset
- Feature backbone functionality
- Embedding generation for downstream tasks
- Flexible integration with PyTorch workflows
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its pure convolutional approach to vision tasks, proving that sophisticated attention mechanisms aren't always necessary for high performance. It achieves competitive results while maintaining architectural simplicity.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks, feature extraction, and generating embeddings for transfer learning applications. It's ideal for scenarios requiring a balance between computational efficiency and classification accuracy.
