LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes

Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade and Ali Farhadi

This repository contains code for the ImageNet-1K classification experiments presented in the NeurIPS 2021 paper along with more functionalities.

This code base is built upon the hidden-networks repository.

The Image retrieval experiments presented in the paper were done using the models obtained for classification and further adapted to ImageNet-100.

Set Up

Clone this repository.
Using Python 3.6, create a venv with python -m venv myenv and run source myenv/bin/activate. You can also use conda to create a virtual environment.
Install requirements with pip install -r requirements.txt for venv and appropriate conda commands for conda environment.
Create a data directory <data-dir>. To run the ImageNet experiments there must be a folder <data-dir>/imagenet that contains the ImageNet train and val folders that contains images of each class in a seperate folder.

Training

This codebase contains model architecture for ResNet50 and support to train them on ImageNet-1K (other model architectures can be added to the same file for ease of utilization). We have provided some config files for training ResNet50 which can be modified for other architectures and datasets. To support more datasets, please add new dataloaders to data folder.

Training across multiple GPUs is supported, however, the user should check the minimum number of GPUs required to scale ImageNet-1K.

Codebook Learning:

We support naive codebook learning with num_bits long codes for num_classes classes and also support warmstarting them. Please check the config file for more potential parameters.

Base: python main.py --config configs/largescale/baselines/resnet50-llc-codebook-learning.yaml --multigpu 0,1,2,3 --save-codebook <path-to-save-codebook>.

Warmstarting codebook: python main.py --config configs/largescale/baselines/resnet50-llc-codebook-learning.yaml --multigpu 0,1,2,3 --load-codebook <path-to-warmstart-codebook> --save-codebook <path-to-save-codebook>.

Instance Code Learning:

For a given codebook and (pretrained) backbone, instance code learning of num_bits long bnary codes. Please check the config file for more potential parameters.

Base: python main.py --config configs/largescale/baselines/resnet50-llc-instance-code-learning.yaml --multigpu 0,1,2,3 --instance-code --load-codebook <path-to-saved-codebook> --pretrained <path-to-pretrained-model>.

While the default decoding scheme is set ot mhd, one can change the scheme to ed by using --decode flag in the command line.

Models and Logging

The saved models are compatible with the traditional dense models for simple evaluation and usage as transfer learning backbones. However, the final layer is decomposed that might result in unforseen issues, so caution advised.

Every experiment creates a directory inside runs folder (which will be created automatically) along with the tensorboard logs, initial model state and best model (model_best.pth) - pretrained model for instance code learning.

The runs folder also has dumps of the csv with final and best accuracies. The code checkpoints after every epoch giving a chance to resume training when pre-empted, the extra functionalities can be explored through python main.py -h.

Evaluating models on ImageNet-1K:

If you want to evaluate a pretrained LLC model provided below, you can use the model as is along with the codebook.

Class Code Evaluation: python main.py --config configs/largescale/baselines/resnet50-llc-codebook-learning.yaml --multigpu 0,1,2,3 --load-codebook <path-to-saved-codebook> --pretrained <path-to-pretrained-model> --evaluate

Instance Code Evaluation: python main.py --config configs/largescale/baselines/resnet50-llc-instance-code-learning.yaml --multigpu 0,1,2,3 --load-codebook <path-to-saved-codebook> --pretrained <path-to-pretrained-model> --evaluate --instance-code --decode <mhd/ed>

Pretrained Models

We provide the 20-bit model trained with ResNet50 backbon on ImageNet-1K according to the settings in the paper.

The Class Code Evaluation of the provided pretrained model and codebook should give a top-1 accuracy of 75.5%. While the Instance Code Evaluation using MHD (Minimum Hamming Distance) gives 74.5% and using ED (Exact Decoding) gives 68.9%.

ResNet50 pretrained backbone projecting to 20 dim space - R50 Pretrained Backbone.

Learnt 20-bit codebook - 20-bit Codebook.

Note that the codebook is stored with the underlying real-values. We recommend people store it this way and binarize the codebook to +1/-1 for usage in LLC.

Citation

If you find this project useful in your research, please consider citing:

@InProceedings{Kusupati21
  author    = {Kusupati, Aditya and Wallingford, Matthew and Ramanujan, Vivek and Somani, Raghav and Park, Jae Sung and Pillutla, Krishna and Jain, Prateek and Kakade, Sham and Farhadi, Ali},
  title     = {LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes},
  booktitle = {Advances in Neural Information Processing Systems},
  month     = {December},
  year      = {2021},
}

A portion of the code has been refactored for ease of usage by William Howard-Snyder with assitance from Gary Geng. Please contact Aditya Kusupati for comments or questions.

RAIVNLab / LLC