PaddlePaddle/PaddleClas
PaddleClas
PaddleClas is a toolkit for image classification and recognition built on PaddlePaddle. It provides a suite of tools for training deep learning models and a framework for implementing visual search and retrieval systems.
The project includes a computer vision model optimization suite and tools for cross-platform deployment. It enables the export of trained models to servers, mobile devices, and edge hardware to achieve high-performance inference across different programming languages.
The toolkit covers model compression and optimization through pruning, quantization, and knowledge distillation. It also supports visual information retrieval by combining object detection, neural feature extraction, and vector search to identify similar images within a dataset.
Users can design end-to-end classification and recognition workflows using a visual workflow builder.
Features
- Image Classification - Provides a comprehensive toolkit for training and deploying deep learning models for image classification.
- Classification Training - Implements workflows for training deep learning models to assign labels to images, including face recognition.
- Image Retrieval Systems - Provides a framework for building image retrieval systems that combine object detection, feature extraction, and vector search.
- Image-to-Image Retrieval - Implements image-to-image retrieval systems to find visually similar samples using image embeddings.
- Deep Feature Extractors - Implements specialized neural networks to extract image features and descriptors for similarity search and classification.
- Visual - Creates systems that extract image features to find and retrieve similar visuals from large datasets.
- Image Embedding Searches - Combines image embeddings and vector indexing to find and retrieve visually similar content.
- Knowledge Distillation - Employs knowledge distillation to transfer patterns from large teacher models to smaller, efficient student models.
- Edge AI Model Deployment - Optimizes and deploys machine learning models to run efficiently on servers, mobile devices, and edge hardware.
- Cross-Platform Deployments - Exports and optimizes vision models for deployment across diverse hardware from edge devices to cloud GPUs.
- Model Exporters - Provides utilities to convert trained models into standardized formats for cross-platform inference.
- Model Optimization Suites - Provides a suite of tools specifically for optimizing model memory and inference performance.
- Model Pruning - Reduces model size and computational requirements by removing redundant parameters and connections.
- Model Compression Suites - Offers a comprehensive toolkit for reducing model size through pruning, quantization, and distillation.
- Model Compression - Reduces the computational requirements and size of neural networks through pruning and quantization.
- Weight Quantization - Compresses model weights into lower-precision formats to accelerate inference on edge and mobile hardware.
- Model Accuracy Optimization - Improves precision and recall using knowledge distillation and advanced learning rate strategies.
- Computer Vision - Provides foundational image classification models and architectures.
- Vision Transformers - Includes implementations for mixing features across windows and dimensions.
Star history
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