# Few-Shot Learning

Theia is a vision foundation model designed for robot learning, constructed by distilling multiple off-the-shelf vision foundation models, possessing rich visual representation capabilities.

Theia is a vision foundation model for robot learning, distilled from multiple off-the-shelf vision foundation models, offering rich visual representation capabilities.

This model is a fine-tuned version of the RobBERT v2 Dutch base model for sentiment analysis tasks, primarily used for sentiment classification of Dutch texts.

A video analysis model fine-tuned based on the VideoMAE foundation model, specializing in eye contact recognition tasks related to Autism Spectrum Disorder (ASD)

Mistral-ORPO-β is a 7B-parameter language model fine-tuned using the ORPO method based on Mistral-7B, capable of directly learning preferences without a supervised fine-tuning warm-up phase.

IDEFICS-9B is a 9-billion-parameter multimodal model capable of processing both image and text inputs to generate text outputs. It is an open-source replication of Deepmind's Flamingo model.

A receipt recognition model fine-tuned based on naver-clova-ix/donut-base

An image classification model fine-tuned on the image folder dataset based on microsoft/beit-base-patch16-224-pt22k-ft22k

An image classification model based on the Vision Transformer architecture, fine-tuned for BIRADS classification tasks

An image classification model fine-tuned on the beans dataset based on Google's ViT model, achieving an accuracy of 97.74%

This is a binary classification model trained with AutoTrain, designed to determine whether objects in images are furry.

A fine-tuned model based on Swin Transformer Tiny architecture for image classification tasks, achieving 100% accuracy on the evaluation set

A text classification model fine-tuned based on microsoft/deberta-v3-large, trained on the SST-2 dataset

A sentiment analysis model fine-tuned on the SST-2 dataset based on microsoft/deberta-v3-large