# Learning to Hash based on Angularly Discriminative Embedding-pytorch This repository is reimplementation of [Learning to Hash based on Angularly Discriminative Embedding] Original repo is [here](https://github.com/sudalvxin/ADE.git). # Requirements - Python 2.7 - PyTorch 0.4 - torchvision 0.3.0 - numpy 1.16.2 # How to run Prepare dataset Running experiments CIFAR10 ```python $ python CIFAR10.py ``` # Note: # [Different data (hash codes, and metrics) generally need different parameters.£© We suggest that \lambda takes a larger value when code length $k$ and class number $c$ take larger value: For example: 48bit, 64 bit For CIFAR10 or SVHN~(mAP) \lambda = 0.1; CIFAR10 or SVHN~(Pre@2) \lambda = 0.3 For Imagenet or CIFAR100~(mAP) \lambda = 0.3; Imagenet or CIFAR100~(Pre@2) \lambda = 1