wanboyang / camel

CaMEL: Mean Teacher Learning for Image Captioning. arXiv 2022.

CaMEL: Mean Teacher Learning for Image Captioning

This repository contains the reference code for the paper CaMEL: Mean Teacher Learning for Image Captioning.

Please cite with the following BibTeX:

@article{barraco2022camel,
  title={{CaMEL: Mean Teacher Learning for Image Captioning}},
  author={Barraco, Manuele and Stefanini, Matteo and Cornia, Marcella and Cascianelli, Silvia and Baraldi, Lorenzo and Cucchiara, Rita},
  journal={arXiv preprint arXiv:2202.10492},
  year={2022}
}

Environment setup

Clone the repository and create the camel_release conda environment using the environment.yml file:

conda env create -f environment.yml
conda activate camel_release

Note: Python 3.8 is required to run our code.

Data preparation

To run the code, annotations and images for the COCO dataset are needed. Please download the zip files containing the images (train2014.zip, val2014.zip), the zip file containing the annotations (annotations_trainval2014.zip) and extract them. These paths will be set as arguments later.

Evaluation

To reproduce the results reported in our paper, download the pretrained model file camel_mesh.pth or camel_nomesh.pth and place it anywhere. Its path will be set as argument later.

Run python evaluation.py using the following arguments:

Argument	Possible values
--batch_size	Batch size (default: 25)
--workers	Number of workers (default: 0)
--resume_last	If used, the training will be resumed from the last checkpoint
--resume_best	If used, the training will be resumed from the best checkpoint
--annotation_folder	Path to folder with COCO annotations (required)
--image_folder	Path to folder with COCO images (required)
--saved_model_path	Path to model weights file (required)
--clip_variant	CLIP variant to be used as image encoder (default: RN50x16)
--network	Network to be used in the evaluation, online or target (default: target)
--disable_mesh	If used, the model does not employ the mesh connectivity
--N_dec	Number of decoder layers (default: 3)
--N_enc	Number of encoder layers (default: 3)
--d_model	Dimensionality of the model (default: 512)
--d_ff	Dimensionality of Feed-Forward layers (default: 2048)
--m	Number of memory vectors (default: 40)
--head	Number of heads (default: 8)

For example, to evaluate our model, use

python evaluate.py --image_folder /path/to/images --annotation_folder /path/to/annotations --saved_model_path /path/to/model_file.pth

Training procedure

Run python train.py using the following arguments:

Argument	Possible values
--exp_name	Experiment name (default: camel)
--batch_size	Batch size (default: 25)
--workers	Number of workers (default: 0)
--resume_last	If used, the training will be resumed from the last checkpoint
--resume_best	If used, the training will be resumed from the best checkpoint
--annotation_folder	Path to folder with COCO annotations (required)
--image_folder	Path to folder with COCO images (required)
--clip_variant	CLIP variant to be used as image encoder (default: RN50x16)
--distillation_weight	Weight for the knowledge distillation loss (default: 0.1 in XE phase, 0.005 in SCST phase)
--ema_weight	Target decay rate of Mean Teacher paradigm (default: 0.999)
--phase	Training phase, xe or scst (default: xe)
--disable_mesh	If used, the model does not employ the mesh connectivity
--saved_model_file	If used, path to model weights to be loaded
--N_dec	Number of decoder layers (default: 3)
--N_enc	Number of encoder layers (default: 3)
--d_model	Dimensionality of the model (default: 512)
--d_ff	Dimensionality of Feed-Forward layers (default: 2048)
--m	Number of memory vectors (default: 40)
--head	Number of heads (default: 8)
--warmup	Warmup value for learning rate scheduling (default: 10000)

For example, to train our model with the parameters used in our experiments, use

python train.py --image_folder /path/to/images --annotation_folder /path/to/annotations