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- Implement Food101 dataset class for few-shot learning support. - Create ImageNet dataset class with preprocessed data handling. - Add ImageNetA, ImageNetR, ImageNetSketch, and ImageNetV2 classes for various ImageNet subsets. - Introduce OxfordFlowers dataset class with data splitting functionality. - Develop OxfordPets dataset class with comprehensive data handling and subsampling. - Implement StanfordCars dataset class with annotation loading. - Create SUN397 dataset class with class name mapping. - Add UCF101 dataset class for action recognition tasks. - Introduce a new training script to manage training configurations and execution. - Extend configuration management to support new dataset and training parameters.
…ve Learning methodology
…ct with detailed methodology
- Introduced ImageNet templates for image classification tasks in `imagenet_templates.py`. - Implemented LoRA (Low-Rank Adaptation) layers in `loralib` including `LoRALayer`, `LinearLoRA`, `Embedding`, and various convolutional layers. - Added utility functions for managing LoRA parameters and saving/loading weights in `utils.py`. - Enhanced multi-head attention with LoRA capabilities in `easymultiheadattention.py`. - Updated `__init__.py` to include new modules from `loralib`.
….sh script for training configurations
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