Awesome
Neural Baby Talk
Docker Setup
This repository provides a Dockerfile for setting up all dependencies and preprocessed data for COCO experiments (normal / robust / NOC). Docker support for Flickr30k experiments is not yet supported. To build the Dockerfile, just execute this from project root:
docker build -t nbt .
Before running the container, you need to get COCO dataset downloaded and kept somewhere in your filesystem. Declare two environment variables:
$COCO_IMAGES
: path to a directory with sub-directories of images astrain2014
,val2014
,test2015
, etc...$COCO_ANNOTATIONS
: path to a directory with annotation files likeinstances_train2014.json
,captions_train2014.json
etc...
These directories will be attached as "volumes" to our docker container for Neural Baby Talk to use within. Run the docker image within a container in an interactive mode (bash session). Get nvidia-docker and execute this command to run the fresh built docker image.
nvidia-docker run --name nbt_container -it \
-v $COCO_IMAGES:/workspace/neuralbabytalk/data/coco/images \
-v $COCO_ANNOTATIONS:/workspace/neuralbabytalk/data/coco/annotations \
--shm-size 8G -p 8888:8888 nbt /bin/bash
Ideally, shared memory size (--shm-size
) of 8GB would be enough. Tune it according to your requirements / machine specifications.
Saved Checkpoints: All checkpoints will be saved in /workspace/neuralbabytalk/save
. From outside the container, execute this to get your checkpoints from this container into the main filesystem:
The container would expose port 8888, which can be used to host tensorboard visualizations.
docker container cp nbt_container:workspace/neuralbabytalk/save /path/to/local/filesystem/save
Skip directly to Training and Evaluation section to execute specified commands within the container.
requirement
Inference:
Data Preparation:
Evaluation:
- coco-caption: Download the modified version of coco-caption and put it under
tools/
Demo
Without detection bbox
With detection bbox
Constraint beam search
This code also involve the implementation of constraint beam search proposed by Peter Anderson. I'm not sure my impmentation is 100% correct, but it works well in conjuction with neural baby talk code. You can refer to this paper for more details. To enable CBS while decoding, please set the following flags:
--cbs True|False : Whether use the constraint beam search.
--cbs_tag_size 3 : How many detection bboxes do we want to include in the decoded caption.
--cbs_mode all|unqiue|novel : Do we allow the repetive bounding box? `novel` is an option only for novel object detection task.
Training and Evaluation
Data Preparation
Head to data/README.md
, and prepare the data for training and evaluation.
Pretrained model
Task | Dataset | Backend | Batch size | Link |
---|---|---|---|---|
Standard image captioning | COCO | Res-101 | 100 | Pre-trained Model |
Standard image captioning | Flickr30k | Res-101 | 50 | Pre-trained Model |
Robust image captioning | COCO | Res-101 | 100 | Pre-trained Model |
Novel object captioning | COCO | Res-101 | 100 | Pre-trained Model |
Standard Image Captioning
Training (COCO)
First, modify the cofig file cfgs/normal_coco_res101.yml
with the correct file path.
python main.py --path_opt cfgs/normal_coco_res101.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30
Evaluation (COCO)
Download Pre-trained model. Extract the tar.zip file and put it under save/
.
python main.py --path_opt cfgs/normal_coco_res101.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30 --inference_only True --beam_size 3 --start_from save/coco_nbt_1024
Training (Flickr30k)
Modify the cofig file cfgs/normal_flickr_res101.yml
with the correct file path.
python main.py --path_opt cfgs/normal_flickr_res101.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30
Evaluation (Flickr30k)
Download Pre-trained model. Extract the tar.zip file and put it under save/
.
python main.py --path_opt cfgs/normal_flickr_res101.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30 --inference_only True --beam_size 3 --start_from save/flickr30k_nbt_1024
Robust Image Captioning
Training
Modify the cofig file cfgs/normal_flickr_res101.yml
with the correct file path.
python main.py --path_opt cfgs/robust_coco.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30
Evaluation (robust-coco)
Download Pre-trained model. Extract the tar.zip file and put it under save/
.
python main.py --path_opt cfgs/robust_coco.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30 --inference_only True --beam_size 3 --start_from save/robust_coco_nbt_1024
Novel Object Captioning
Training
Modify the cofig file cfgs/noc_coco_res101.yml
with the correct file path.
python main.py --path_opt cfgs/noc_coco_res101.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30
Evaluation (noc-coco)
Download Pre-trained model. Extract the tar.zip file and put it under save/
.
python main.py --path_opt cfgs/noc_coco_res101.yml --batch_size 20 --cuda True --num_workers 20 --max_epoch 30 --inference_only True --beam_size 3 --start_from save/noc_coco_nbt_1024
Multi-GPU Training
This codebase also support training with multiple GPU. To enable this feature, simply add --mGPUs Ture
in the commnad.
Self-Critic Training and Fine-Tuning CNN
This codebase also support self-critic training and fine-tuning CNN. You are welcome to try this part and upload your trained model to the repo!
More Visualization Results
Reference
If you use this code as part of any published research, please acknowledge the following paper
@inproceedings{Lu2018Neural,
author = {Lu, Jiasen and Yang, Jianwei and Batra, Dhruv and Parikh, Devi},
title = {Neural Baby Talk},
booktitle = {CVPR},
year = {2018}
}
Acknowledgement
We thank Ruotian Luo for his self-critical.pytorch repo.