lxztju/pytorch_classification

Health v2 · maintenance only

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1466 stars · 343 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A complete PyTorch image classification codebase built on torchvision, covering training, prediction, TTA, model ensembling, knowledge distillation, and CNN feature extraction with classifiers like SVM and random forest. It also includes deployment options via Flask + Redis, C++ libtorch, and TensorRT inference.

Use cases

• train an image classification model in pytorch
• deploy a trained cnn model with flask or libtorch
• perform test-time augmentation (tta) for predictions
• ensemble multiple models with weighted or voting fusion
• extract cnn features and classify with svm or random forest
• apply knowledge distillation to a smaller model
• export a pytorch model to tensorrt or c++ for inference

When to choose

• you want an end-to-end pytorch image classification pipeline with training and deployment
• you need torchvision-based models like resnet, densenet, efficientnet, or swin transformer with modern training tricks
• you want to deploy models in c++ via libtorch or tensorrt
• you need feature extraction plus classical classifiers like svm or random forest

When to avoid

• you need a maintained library with frequent updates and official support
• you want object detection, segmentation, or other vision tasks beyond classification
• you need a high-level training API like pytorch lightning with minimal boilerplate
• you are not working in python or c++ environments

Facets

library · maturity maintenance

machine-learning image-processing llm-training deployment computer-vision image-processing deep-learning machine-learning python cpp image-classification pytorch torchvision model-deployment knowledge-distillation tta libtorch tensorrt model-ensemble flask-api gpu

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