Forward Compatible Training (FCT)

This repository is a PyTorch implementation of Forward Compatible Training for Large-Scale Embedding Retrieval Systems and can be used to reproduce the results in the paper.

The code is written to use Python 3.7 or above.

Requirements

We suggest you first create a virtual environment and install dependencies in the virtual environment.

# Go to repo
cd <path/to/ml-fct>
# Create virtual environment ...
python -m venv .venv
# ... and activate it
source .venv/bin/activate
# Upgrade to the latest versions of pip and wheel
pip install -U pip wheel
pip install -r requirements.txt

Quick Start (CIFAR100)

We provide CIFAR-100 experiment, for fast exploration. Here are the sequence of commands for cifar100 experiments (similar to ImageNet but faster cycles.):

# Get data: following command put data in data_store/cifar-100-python
python prepare_dataset.py

# Train old embedding model:
# Note: config files assume training with 8 GPUs. Modify them according to your environment.
python train_backbone.py --config configs/cifar100_backbone_old.yaml

# Evaluate the old model:
python eval.py --config configs/cifar100_eval_old_old.yaml

# Train New embedding model:
python train_backbone.py --config configs/cifar100_backbone_new.yaml

# Evaluate the new model:
python eval.py --config configs/cifar100_eval_new_new.yaml

# Download pre-traianed models if training with side-information:
source get_pretrained_models.sh

# Train FCT transformation:
# If training with side-info model add its path to the config file below. You
# can use the same side-info model as for ImageNet experiment here. 

python train_transformation.py --config configs/cifar100_transformation.yaml

# Evaluate transformed model vs new model:
python eval.py --config configs/cifar100_eval_old_new.yaml

Case	CMC Top-1 (%)	CMC Top-5 (%)	mAP (%)
old/old	32.9	59.3	16.1
new/new	56.4	77.5	36.5
new/old	50.6	74.2	34.2

ImageNet Experiment

Here are the sequence of commands for ImageNet experiments:

# Get data: Prepare full ImageNet-1k dataset and provide its path in all config
# files. The path should include training and validation directories. 

# Train old embedding model:
# Note: config files assume training with 8 GPUs. Modify them according to your environment.
python train_backbone.py --config configs/imagenet_backbone_old.yaml

# Evaluate the old model:
python eval.py --config configs/imagenet_eval_old_old.yaml

# Train New embedding model:
python train_backbone.py --config configs/imagenet_backbone_new.yaml

# Evaluate the new model:
python eval.py --config configs/imagenet_eval_new_new.yaml

# Download pre-traianed models if training with side-information:
source get_pretrained_models.sh

# Train FCT transformation:
# (If training with side-info model add its path to the config file below)
python train_transformation.py --config configs/imagenet_transformation.yaml

# Evaluate transformed model vs new model:
python eval.py --config configs/imagenet_eval_old_new.yaml

Case	CMC Top-1 (%)	CMC Top-5 (%)	mAP (%)
old/old	46.4	65.1	28.3
new/new	68.4	84.7	45.6
new/old	65.1	82.7	44.0

Contact

• Hadi Pouransari: mpouransari@apple.com

Citation

@article{ramanujan2022forward,
  title={Forward Compatible Training for Large-Scale Embedding Retrieval Systems},
  author={Ramanujan, Vivek and Vasu, Pavan Kumar Anasosalu and Farhadi, Ali and Tuzel, Oncel and Pouransari, Hadi},
  journal={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2022}
}