jacobgil/pytorch-pruning
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference
This helps deep learning practitioners make their image classification models smaller and faster. By taking an existing VGG16-based image classifier and a dataset of categorized images, it produces a more efficient version of the model. Data scientists and machine learning engineers working with image recognition tasks would find this useful.
887 stars. No commits in the last 6 months.
Use this if you need to deploy VGG16-based image classification models to environments with limited computational resources or strict latency requirements.
Not ideal if you are working with non-VGG16 model architectures or require highly precise control over individual pruning steps in a single pass.
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Last pushed
Jul 12, 2019
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