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Vision Transformer and MLP-Mixer architectures for image recognition and processing.

The Vision Transformer (ViT) is a deep learning model architecture based on the Transformer, originally designed for natural language processing, adapted for computer vision tasks. ViT models break down an image into patches, treat these patches as tokens, and input them into a Transformer encoder. This architecture allows the model to capture global relationships between image regions, enabling it to achieve state-of-the-art performance on image classification tasks. The repository provides JAX/Flax implementations of ViT and MLP-Mixer models, pre-trained on ImageNet and ImageNet-21k datasets. It includes code for fine-tuning these models, allowing users to adapt them to specific datasets and tasks. The models were originally trained in the Big Vision codebase, offering advanced features like multi-host training.
The Vision Transformer (ViT) is a deep learning model architecture based on the Transformer, originally designed for natural language processing, adapted for computer vision tasks.
Explore all tools that specialize in fine-tuning. This domain focus ensures Vision Transformer delivers optimized results for this specific requirement.
Explore all tools that specialize in classify images. This domain focus ensures Vision Transformer delivers optimized results for this specific requirement.
Explore all tools that specialize in segment images. This domain focus ensures Vision Transformer delivers optimized results for this specific requirement.
Explore all tools that specialize in train machine learning models. This domain focus ensures Vision Transformer delivers optimized results for this specific requirement.
Provides models pre-trained on large datasets like ImageNet and ImageNet-21k.
Written in JAX and Flax, providing efficient and scalable numerical computation.
Provides code and examples for fine-tuning pre-trained models on custom datasets.
Includes an implementation of the MLP-Mixer architecture, an alternative to Transformers.
Supports various data augmentation techniques to improve model robustness.
Implements Sharpness-Aware Training (SAT) to improve model generalization by minimizing the surrogate gap.
Install Python >= 3.10.
Install JAX and required dependencies using `pip install -r vit_jax/requirements.txt` (for GPU) or `pip install -r vit_jax/requirements-tpu.txt` (for TPU).
Install Flaxformer following the instructions in its repository.
Download pre-trained models from the specified GCS bucket (gs://vit_models/imagenet21k or gs://mixer_models/imagenet21k).
Configure the fine-tuning script with the appropriate dataset and model parameters.
Run the fine-tuning script using `python -m vit_jax.main --workdir=/tmp/vit-$(date +%s) --config=$(pwd)/vit_jax/configs/vit.py:b16,cifar10 --config.pretrained_dir='gs://vit_models/imagenet21k'`.
Monitor the training progress using TensorBoard or similar tools.
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