# ALBEF **Repository Path**: sadsriver/ALBEF ## Basic Information - **Project Name**: ALBEF - **Description**: No description available - **Primary Language**: Unknown - **License**: BSD-3-Clause - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-11-09 - **Last Updated**: 2025-11-09 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Align before Fuse: Vision and Language Representation Learning with Momentum Distillation, NeurIPS 2021 Spotlight (Salesforce Research). ## Announcement: ALBEF is now officially integrated into [LAVIS](https://github.com/salesforce/LAVIS) - a one-stop library for language-and-vision research and applications! This is the official PyTorch implementation of the ALBEF paper [Blog]. This repository supports pre-training on custom datasets, as well as finetuning on VQA, SNLI-VE, NLVR2, Image-Text Retrieval on MSCOCO and Flickr30k, and visual grounding on RefCOCO+. Pre-trained and finetuned checkpoints are released.

### Requirements: * pytorch 1.8.0 * transformers 4.8.1 * timm 0.4.9 ### Download: * Pre-trained checkpoint [[14M](https://storage.googleapis.com/sfr-pcl-data-research/ALBEF/ALBEF.pth)] / [[4M](https://storage.googleapis.com/sfr-pcl-data-research/ALBEF/ALBEF_4M.pth)] * Dataset json files for downstream tasks * Dataset json files for pre-training (the image paths in each json file need to be changed to your own directory) * Finetuned checkpoint for retrieval on MSCOCO * Finetuned checkpoint for retrieval on Flickr30k * Finetuned checkpoint for VQA * Finetuned checkpoint for visual grounding on RefCOCO+ ### Visualization: We provide code in visualize.ipynb to visualize the important areas in an image for each word in a text. Here is an example visualization using the visual grounding checkpoint. Try the Replicate demo here [![Replicate](https://replicate.com/salesforce/albef/badge)](https://replicate.com/salesforce/albef).

### Pre-training on custom datasets: 1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 2. In configs/Pretrain.yaml, set the paths for the json files. 3. Pre-train the model using 8 A100 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --use_env Pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain

### Image-Text Retrieval: 1. Download MSCOCO or Flickr30k datasets from the original websites. 2. Download and extract the provided dataset json files. 3. In configs/Retrieval_coco.yaml or configs/Retrieval_flickr.yaml, set the paths for the json files and the image path. 4. Finetune the pre-trained checkpoint using 8 A100 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --use_env Retrieval.py \
--config ./configs/Retrieval_flickr.yaml \
--output_dir output/Retrieval_flickr \
--checkpoint [Pretrained checkpoint]

### VQA: 1. Download VQA v2 dataset and Visual Genome dataset from the original websites. 2. Download and extract the provided dataset json files. 3. In configs/VQA.yaml, set the paths for the json files and the image paths. 4. Finetune the pre-trained checkpoint using 8 A100 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --use_env VQA.py \
--config ./configs/VQA.yaml \
--output_dir output/vqa \
--checkpoint [Pretrained checkpoint]

5. Evaluate the result using the official evaluation server. ### Visual Entailment: 1. Download SNLI-VE dataset from the original website. 2. Download and extract the provided dataset json files. 3. In configs/VE.yaml, set the paths for the json files and the image path. 4. Finetune the pre-trained checkpoint using 8 A100 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --use_env VE.py \
--config ./configs/VE.yaml \
--output_dir output/VE \
--checkpoint [Pretrained checkpoint]

### Visual Grounding on RefCOCO+: 1. Download MSCOCO dataset from the original website. 2. Download and extract the provided dataset json files. 3. In configs/Grounding.yaml, set the paths for the json files and the image path. 4. Finetune the pre-trained checkpoint using 8 A100 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --use_env Grounding.py \
--config ./configs/Grounding.yaml \
--output_dir output/RefCOCO \
--gradcam_mode itm \ 
--block_num 8 \
--checkpoint [Pretrained checkpoint]

### NLVR2: NLVR2 requires an additional pre-training step with text-assignment (TA) to adapt the model for image-pair inputs. In order to perform TA, first set the paths for the json training files in configs/NLVR_pretrain.yaml, then run:

python -m torch.distributed.launch --nproc_per_node=8 --use_env Pretrain_nlvr.py \
--config ./configs/NLVR_pretrain.yaml \
--output_dir output/NLVR_pretrain \
--checkpoint [Pretrained checkpoint]

We provide the checkpoint after TA pre-training, which can be fine-tuned with the following steps. 1. Download NLVR2 dataset from the original website. 2. Download and extract the provided dataset json files. 3. In configs/NLVR.yaml, set the paths for the json files and the image path. 4. Finetune the pre-trained checkpoint using 8 A100 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --use_env NLVR.py \
--config ./configs/NLVR.yaml \
--output_dir output/NLVR \
--checkpoint [TA pretrained checkpoint]

### Citation If you find this code to be useful for your research, please consider citing.

@inproceedings{ALBEF,
      title={Align before Fuse: Vision and Language Representation Learning with Momentum Distillation}, 
      author={Junnan Li and Ramprasaath R. Selvaraju and Akhilesh Deepak Gotmare and Shafiq Joty and Caiming Xiong and Steven Hoi},
      year={2021},
      booktitle={NeurIPS},
}