Official Implementation of "Grounding Language Models for Visual Entity Recognition". Code, checkpoints and documents are being updated.
Authors: Zilin Xiao, Ming Gong, Paola Cascante-Bonilla, Xingyao Zhang, Jie Wu, Vicente Ordonez
TBD
Training an AutoVER-7B takes 2.5 days on 32 V100 GPUs approximately.
deepspeed --num_gpus 8 --num_nodes 4 train_oven_ret.py \
--deepspeed ./deepspeed_configs/zero3.json \
--model_name_or_path ./vicuna-7b-v1.5-32064-vocab \
--version v1 \
--train_jsonl_path ./ms_jsonl_dataset/oven_entity_train_0.5.jsonl \
--query_jsonl_path ./ms_jsonl_dataset/oven_query_train.jsonl \
--train_img_shards_path $DATASET_PATH/entity_oven/ \
--qid_to_summary_path ./trie_bins/wiki_qid_to_summary_indata.json \
--entity_img_path $DATASET_PATH/wikipedia_images_full \
--retrieval True \
--vision_tower openai/clip-vit-large-patch14-336 \
--pretrain_mm_mlp_adapter ./checkpoints/llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5/mm_projector.bin \
--mm_projector_type mlp2x_gelu \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--image_aspect_ratio pad \
--fp16 \
--output_dir ./checkpoints/llava-v1.5-7b-ms-ret-0.5-trm-feedback2-fp16 \
--num_train_epochs 1 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 5000 \
--save_total_limit 2 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--model_max_length 512 \
--dataloader_num_workers 4 \
--gradient_checkpointing True \
--report_to wandb \
--run_name llava-7b-ms-ret-0.5-trm-feedback2-fp16 \
--log_on_each_node False \
--one_class_per_batch \
--gather_contrastive True \
--entity_encoder_fusion trm \
--ret_to_entity_only True \
--unfreeze_entity_clip FalseSubmit queries to GPT4-V endpoints with baselines/submit_gpt4v.py.
We have provided raw response from GPT4-V in baselines/gpt4v-eval-oven-0.1.jsonl and baselines/gpt4v-query-oven-0.5.jsonl.
Our implementation partially relies on the following repositories:
@inproceedings{xiao2024grounding,
Author = {Zilin Xiao and Ming Gong and Paola Cascante-Bonilla and Xingyao Zhang and Jie Wu and Vicente Ordonez},
Title = {Grounding Language Models for Visual Entity Recognition},
Year = {2024},
Eprint = {arXiv:2402.18695},
}