NielsRogge/Transformers-Tutorials

Features

• Transformer Implementations - Serves as a comprehensive guide for implementing and deploying transformer architectures across various ML domains.
• Transformer Tutorials - Offers a comprehensive collection of tutorials and notebooks for implementing transformer-based architectures across various modalities.
• Computer Vision - Provides a wide range of computer vision implementations including object detection, segmentation, and depth estimation.
• Implementation Guides - Provides implementation examples for deploying models that combine text, image, and audio inputs.
• Model Fine-Tuning - Provides practical demonstrations for adapting pre-trained transformer models to custom datasets using training loops.
• Model Fine-tuning Resources - Provides code samples and educational materials for optimizing pre-trained models using training loops and accelerators.
• Multimodal Processing - Implements techniques for combining text, image, and audio data into shared latent spaces for cross-modal analysis.
• Natural Language Processing - Implements natural language processing tasks such as text classification, document information extraction, and image captioning.
• Pre-trained Model Application - Demonstrates how to use weights from pre-trained transformer networks to perform predictions on new data.
• Supervised Fine-Tuning - Includes resources for optimizing pre-trained models through supervised fine-tuning on custom labeled datasets.
• Object Detection - Provides implementation examples for identifying and locating specific objects within images using bounding boxes.
• Depth Estimation - Implements algorithms to calculate the distance of objects from the camera using single-image depth estimation.
• Image Segmentation - Provides guides for isolating specific objects or semantic regions within images using segmentation techniques.
• Synthetic Media Generators - Includes examples for generating synthetic images from text prompts and creating descriptive captions for media.
• Hardware Acceleration - Provides examples for utilizing GPUs to accelerate the training and inference of transformer models.
• Data Preparation Tools - Offers utilities and examples for cleaning and transforming raw data into formats suitable for transformer model ingestion.
• Tensor Conversion Utilities - Provides examples for preparing PyTorch datasets by converting raw files into tensors and batches.
• Text Classifiers - Demonstrates the categorization of text sequences and audio signals into predefined labels.
• Zero-Shot Inference - Covers methods for performing classification or segmentation on new categories without specific training data.
• Document Parsing and Extraction - Demonstrates how to parse document images to classify layouts and extract structured information.
• Audio Event Classification - Implements models to categorize audio files and signals into specific labels using transformer architectures.
• Dataset Tensor Mappings - Provides examples for converting raw input files into tensors and batches for efficient model processing.
• Dataset Preparation Tutorials - Includes guides for converting raw files into tensors and batches for PyTorch machine learning pipelines.

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