Skip to content

Latest commit

 

History

History
279 lines (248 loc) · 14.6 KB

MODEL_ZOO.md

File metadata and controls

279 lines (248 loc) · 14.6 KB
Raw

Table of Contents

Performance

Task t2i t2i i2t i2t IC IC IC IC NoCaps NoCaps VQA NLVR2
Metric R@1 R@5 R@1 R@5 B@4 M C S C S test-std test-P
SoTA_S 39.2 68.0 56.6 84.5 38.9 29.2 129.8 22.4 61.5 9.2 70.90 53.50
SoTA_B 48.4 76.7 63.3 87.0 39.5 29.3 129.3 23.2 73.1 11.2 72.54 78.87
SoTA_L 51.7 78.4 66.6 89.4 - - - - - - 73.40 79.50
----- --- --- --- --- --- --- --- --- --- --- --- ---
Oscar_B 54.0 80.8 70.0 91.1 40.5 29.7 137.6 22.8 78.8 11.7 73.44 78.44
Oscar_L 57.5 82.8 73.5 92.2 41.7 30.6 140.0 24.5 80.9 11.3 73.82 80.37
gain 5.8 4.4 6.9 2.8 2.2 1.3 10.7 1.3 7.8 0.5 0.42 0.87

t2i: text-to-image retrieval; i2t: image-to-text retrieval; IC: image captioning on COCO.

For reference, we also release the training logs and output.

VQA

Script to finetune for Oscar base model. Base model is trained on train split and evaluated on the val split. Good for later comparison.

Training logs: eval_logs.json, output.txt.
Final server results: results.txt.
Model checkpoint: .zip.

python oscar/run_vqa.py -j 4 --img_feature_dim 2054 --max_img_seq_length
    50 --data_label_type mask --img_feature_type faster_r-cnn --data_dir datasets/vqa/2k
    --model_type bert --model_name_or_path pretrained_models/base-vg-labels/ep_107_1192087
    --task_name vqa_text --do_train --do_lower_case --max_seq_length 128 --per_gpu_eval_batch_size
    256 --per_gpu_train_batch_size 32 --learning_rate 5e-05 --num_train_epochs 25
    --output_dir results --label_file datasets/vqa/cache/trainval_ans2label.pkl
    --save_epoch 1 --seed 88 --evaluate_during_training --logging_steps 4000 --drop_out
    0.3 --weight_decay 0.05 --warmup_steps 0 --loss_type bce --img_feat_format pt 
    --classifier linear --cls_hidden_scale 3 --txt_data_dir datasets/vqa/2k

Script to finetune for Oscar large model. Large model is trained on train+val split and evaluated on the val split, for reproduce the paper's best result.

Training logs: eval_logs.json, output.txt.
Final server results: results.txt.
Model checkpoint: .zip.

python oscar/run_vqa.py -j 4 --img_feature_dim 2054 --max_img_seq_length
    50 --data_label_type mask --img_feature_type faster_r-cnn --data_dir datasets/vqa/2k
    --model_type bert --model_name_or_path pretrained_models/large-vg-labels/ep_20_590000
    --task_name vqa_text --do_train_val --do_lower_case --max_seq_length 128 --per_gpu_eval_batch_size
    256 --per_gpu_train_batch_size 24 --learning_rate 3e-05 --num_train_epochs 25
    --label_file datasets/vqa/cache/trainval_ans2label.pkl --save_epoch 30
    --seed 88 --evaluate_during_training --logging_steps 4000 --drop_out 0.3 --weight_decay
    0.05 --warmup_steps 0 --loss_type bce --save_after_epoch 15 --output_dir results --img_feat_format pt --classifier linear --cls_hidden_scale 3 --txt_data_dir datasets/vqa/2k

GQA

Script to finetune for Oscar base model.

Training logs: eval_logs.json, output.txt.
Final server results: results.txt.
Model checkpoint: .zip.

python oscar/run_gqa.py -j 4 --img_feature_dim 2054 --max_img_seq_length
    45 --data_dir datasets/GQA/0.4true --model_type bert --model_name_or_path pretrained_models/base-vg-labels/ep_107_1192087
    --task_name gqa --do_lower_case --max_seq_length 165 --per_gpu_eval_batch_size
    256 --per_gpu_train_batch_size 48 --learning_rate 5e-05 --num_train_epochs 5 --output_dir
    results --label_file datasets/GQA/questions1.2/trainval_testdev_all_ans2label.pkl
    --img_feature_type faster_r-cnn --data_label_type all --train_data_type all --eval_data_type
    bal --label2ans_file datasets/GQA/questions1.2/trainval_testdev_all_label2ans.pkl
    --loss_type xe --save_epoch 2 --seed 88 --evaluate_during_training --logging_steps
    4000 --drop_out 0.3 --do_train --weight_decay 0.05 --warmup_steps 0

NLVR2

Script to finetune for Oscar base model.

Training logs: eval_logs.json, output.txt.
Final server results: results.txt.

python oscar/run_nlvr.py -j 4 --img_feature_dim 2054 --max_img_seq_length
    40 --data_dir datasets/nlvr2/ft_corpus --model_type bert --model_name_or_path pretrained_models/base-vg-labels/ep_107_1192087
    --task_name nlvr --do_lower_case --max_seq_length 55 --per_gpu_eval_batch_size
    64 --per_gpu_train_batch_size 72 --learning_rate 3e-05 --num_train_epochs 20 --output_dir
    results --img_feature_type faster_r-cnn --data_label_type all --train_data_type
    all --eval_data_type all --loss_type xe --save_epoch -1 --seed 88 --evaluate_during_training
    --logging_steps -1 --drop_out 0.3 --do_train --weight_decay 0.05 --warmup_steps
    10000 --classifier mlp --cls_hidden_scale 3 --num_choice 2 --use_pair

Script to finetune for Oscar large model.

Training logs: eval_logs.json, output.txt.
Final server results: results.txt.

python oscar/run_nlvr.py -j 4 --img_feature_dim 2054 --max_img_seq_length
    40 --data_dir datasets/nlvr2/ft_corpus --model_type bert --model_name_or_path pretrained_models/large-vg-labels/ep_55_1617000
    --task_name nlvr --do_lower_case --max_seq_length 55 --per_gpu_eval_batch_size
    64 --per_gpu_train_batch_size 24 --learning_rate 3e-05 --num_train_epochs 20 --output_dir
    results --img_feature_type faster_r-cnn --data_label_type all --train_data_type
    all --eval_data_type all --loss_type xe --save_epoch -1 --seed 88 --evaluate_during_training
    --logging_steps -1 --drop_out 0.3 --do_train --weight_decay 0.05 --warmup_steps
    5000 --classifier mlp --cls_hidden_scale 2 --num_choice 2 --use_pair

Image Text Retrieval

Script to finetune for Oscar base model (4 V100 with 16G mem):

Training logs: eval_logs.json, log.txt. Model checkpoint: checkpoint.zip.

python oscar/run_retrieval.py \
    --model_name_or_path pretrained_models/base-vg-labels/ep_67_588997 \
    --do_train \
    --do_lower_case \
    --evaluate_during_training \
    --num_captions_per_img_val 20 \
    --eval_caption_index_file minival_caption_indexs_top20.pt \
    --per_gpu_train_batch_size 32 \
    --learning_rate 0.00002 \
    --num_train_epochs 30 \
    --weight_decay 0.05 \
    --save_steps 5000 \
    --add_od_labels \
    --od_label_type vg \
    --max_seq_length 70 \
    --output_dir output/

Script to finetune for Oscar large model (8 V100 with 32G mem):

Training logs: eval_logs.json, log.txt. Model checkpoint: checkpoint.zip.

python oscar/run_retrieval.py \
    --model_name_or_path pretrained_models/large-vg-labels/ep_7_816000 \
    --do_train \
    --do_lower_case \
    --evaluate_during_training \
    --num_captions_per_img_val 20 \
    --eval_caption_index_file minival_caption_indexs_top20.pt \
    --per_gpu_train_batch_size 16 \
    --learning_rate 0.00001 \
    --num_train_epochs 30 \
    --save_steps 5000 \
    --add_od_labels \
    --od_label_type vg \
    --max_seq_length 70 \
    --output_dir output/

Script to inference on COCO 1K test set:

python oscar/run_retrieval.py \
    --do_test \
    --do_eval \
    --test_split test \
    --num_captions_per_img_val 5 \
    --eval_img_keys_file test_img_keys_1k.tsv \
    --cross_image_eval \
    --per_gpu_eval_batch_size 64 \
    --eval_model_dir your_model_for_evaluation # could be base/large models.

Script to inference on COCO 5K test set:

python oscar/run_retrieval.py \
    --do_test \
    --do_eval \
    --test_split test \
    --num_captions_per_img_val 5 \
    --eval_img_keys_file test_img_keys.tsv \
    --cross_image_eval \
    --per_gpu_eval_batch_size 64 \
    --eval_model_dir your_model_for_evaluation # could be base/large models.

Image Captioning on COCO

Script to finetune for Oscar base model (4 V100 with 16G mem):

Training logs: log.txt. Model checkpoint: checkpoint.zip.

  1. First train with cross-entropy loss:
python oscar/run_captioning.py \
    --model_name_or_path pretrained_models/base-vg-labels/ep_67_588997 \
    --do_train \
    --do_lower_case \
    --evaluate_during_training \
    --add_od_labels \
    --learning_rate 0.00003 \
    --per_gpu_train_batch_size 64 \
    --num_train_epochs 30 \
    --save_steps 5000 \
    --output_dir output/
  1. Finetune with CIDEr optimization:
python oscar/run_captioning.py \
    --model_name_or_path your_checkpoint_from_cross_entropy \
    --do_train \
    --do_lower_case \
    --evaluate_during_training \
    --add_od_labels \
    --learning_rate 0.000005 \
    --per_gpu_train_batch_size 16 \
    --num_train_epochs 5 \
    --scst \
    --save_steps 2000 \
    --output_dir output/

Script to finetune for Oscar large model (8 V100 with 32G mem):

  1. First train with cross-entropy loss:
python oscar/run_captioning.py \
    --model_name_or_path pretrained_models/large-vg-labels/ep_7_816000 \
    --do_train \
    --do_lower_case \
    --evaluate_during_training \
    --add_od_labels \
    --learning_rate 0.00001 \
    --per_gpu_train_batch_size 32 \
    --num_train_epochs 30 \
    --save_steps 5000 \
    --output_dir output/
  1. Finetune with CIDEr optimization:
python oscar/run_captioning.py \
    --model_name_or_path your_checkpoint_from_cross_entropy \
    --do_train \
    --do_lower_case \
    --evaluate_during_training \
    --add_od_labels \
    --learning_rate 0.000005 \
    --per_gpu_train_batch_size 8 \
    --num_train_epochs 5 \
    --scst \
    --save_steps 2000 \
    --output_dir output/

Script to inference on COCO test set:

python oscar/run_captioning.py \
    --do_test \
    --do_eval \
    --test_yaml test.yaml \
    --per_gpu_eval_batch_size 64 \
    --num_beams 5 \
    --max_gen_length 20 \
    --eval_model_dir your_model_for_evaluation # could be bert base/large.