| Property | Value |
|---|---|
| Parameter Count | 15.4M |
| License | Apache-2.0 |
| Image Size | 224x224 |
| Framework | PyTorch (timm) |
| Paper | ResMLP Paper |
What is resmlp_12_224.fb_in1k?
ResMLP-12/224 is a feedforward neural network designed specifically for image classification tasks. Developed by Facebook Research, this model represents a innovative approach to computer vision that relies purely on Multi-Layer Perceptron (MLP) architecture, moving away from traditional convolutional or attention-based methods.
Implementation Details
The model features a carefully designed architecture with 15.4M parameters, operating on 224x224 pixel images. It achieves 3.0 GMACs computational efficiency with 5.5M activations, making it relatively lightweight for its capabilities.
- Data-efficient training methodology on ImageNet-1k dataset
- Pure feedforward architecture without convolutions
- Optimized for both classification and feature extraction tasks
Core Capabilities
- Image classification with state-of-the-art accuracy
- Feature extraction for downstream tasks
- Efficient processing of 224x224 resolution images
- Supports both classification and embedding generation
Frequently Asked Questions
Q: What makes this model unique?
ResMLP stands out for its pure MLP-based architecture, achieving competitive performance without using convolutions or attention mechanisms. This makes it an interesting alternative to traditional CNN-based models while maintaining efficiency.
Q: What are the recommended use cases?
The model is ideal for image classification tasks and can be used as a feature extractor for transfer learning. It's particularly suitable for applications requiring a good balance between computational efficiency and accuracy.