MLP-Mixer B/16-224
| Property | Value |
|---|---|
| Parameter Count | 59.9M |
| GMACs | 12.6 |
| Image Size | 224x224 |
| Paper | MLP-Mixer: An all-MLP Architecture for Vision |
| Pretrained Dataset | ImageNet-21k |
| Fine-tuned Dataset | ImageNet-1k |
What is mixer_b16_224.goog_in21k_ft_in1k?
The MLP-Mixer B/16-224 is an innovative vision model that challenges traditional convolutional neural network architectures by relying entirely on multi-layer perceptrons (MLPs) for image processing. This particular implementation has been pretrained on the extensive ImageNet-21k dataset and subsequently fine-tuned on ImageNet-1k, making it highly effective for general image classification tasks.
Implementation Details
This model represents a significant departure from conventional vision architectures, utilizing a patch-based approach where images are divided into 16x16 patches and processed through dedicated MLP layers. With 59.9M parameters and 12.6 GMACs, it achieves an efficient balance between computational cost and performance.
- Processes 224x224 pixel images
- Uses separate MLPs for spatial and channel mixing
- Features 14.5M activations
- Implementation available through the timm library
Core Capabilities
- Image classification with 1000 classes (ImageNet-1k)
- Feature extraction for downstream tasks
- Efficient processing of visual information without convolutions
- Supports both classification and embedding generation
Frequently Asked Questions
Q: What makes this model unique?
This model is unique in its pure MLP-based architecture, completely avoiding convolutions and attention mechanisms while still achieving competitive performance. It demonstrates that simple, well-designed MLPs can effectively process visual data.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, particularly when working with standard resolution images (224x224). It can be used for both direct classification and as a feature extractor for transfer learning applications. The model is particularly effective when pretrained knowledge from the large-scale ImageNet-21k dataset is beneficial.
