gMLP S16 224 RA3
| Property | Value |
|---|---|
| Parameter Count | 19.4M |
| Image Size | 224 x 224 |
| License | Apache-2.0 |
| Paper | Pay Attention to MLPs |
| Framework | PyTorch (timm) |
What is gmlp_s16_224.ra3_in1k?
The gMLP S16 224 RA3 is an innovative image classification model that represents a departure from traditional transformer-based architectures. Developed as part of the "Pay Attention to MLPs" research, this model demonstrates that MLPs can be effectively used for vision tasks without relying heavily on attention mechanisms.
Implementation Details
This model implementation features a sophisticated architecture with 19.4M parameters and operates on 224x224 pixel images. It achieves 4.4 GMACs (Giga Multiply-Accumulate Operations) and maintains 15.1M activations during processing. The model is implemented in the timm library and trained on the ImageNet-1k dataset.
- Efficient parameter utilization with 19.4M parameters
- Optimized for 224x224 image processing
- Trained on ImageNet-1k for robust image classification
- Implements the gMLP architecture with spatial gating
Core Capabilities
- High-performance image classification
- Feature extraction for downstream tasks
- Efficient processing with moderate computational requirements
- Support for both classification and embedding generation
Frequently Asked Questions
Q: What makes this model unique?
This model showcases the effectiveness of MLP-based architectures in computer vision, challenging the notion that attention mechanisms are necessary for high performance. It achieves competitive results while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, feature extraction, and generating image embeddings. It's particularly effective for applications requiring balanced performance and computational efficiency with standard resolution images.
