Images download
Image source | Download path |
---|---|
OKVQA | annotations images |
gqa | annotations images |
hateful meme | images and annotations |
iconqa | images and annotation |
vizwiz | images and annotation |
RefCOCO | annotations |
RefCOCO+ | annotations |
RefCOCOg | annotations |
${MINIGPTv2_EVALUATION_DATASET}
├── gqa
│ └── test_balanced_questions.json
│ ├── testdev_balanced_questions.json
│ ├── gqa_images
├── hateful_meme
│ └── hm_images
│ ├── dev.jsonl
├── iconvqa
│ └── iconvqa_images
│ ├── choose_text_val.json
├── vizwiz
│ └── vizwiz_images
│ ├── val.json
├── vsr
│ └── vsr_images
├── okvqa
│ ├── okvqa_test_split.json
│ ├── mscoco_val2014_annotations_clean.json
│ ├── OpenEnded_mscoco_val2014_questions_clean.json
├── refcoco
│ └── instances.json
│ ├── refs(google).p
│ ├── refs(unc).p
├── refcoco+
│ └── instances.json
│ ├── refs(unc).p
├── refercocog
│ └── instances.json
│ ├── refs(google).p
│ ├── refs(und).p
...
export PYTHONPATH=$PYTHONPATH:/path/to/directory/of/MiniGPT-4
Set llama_model to the path of LLaMA model.
Set ckpt to the path of our pretrained model.
Set eval_file_path to the path of the annotation files for each evaluation data.
Set img_path to the img_path for each evaluation dataset.
Set save_path to the save_path for each evaluation dataset.
in eval_configs/minigptv2_benchmark_evaluation.yaml
port=port_number
cfg_path=/path/to/eval_configs/minigptv2_benchmark_evaluation.yaml
dataset names:
refcoco | refcoco+ | refcocog |
---|
torchrun --master-port ${port} --nproc_per_node 1 eval_ref.py \
--cfg-path ${cfg_path} --dataset refcoco,refcoco+,refcocog --resample
port=port_number
cfg_path=/path/to/eval_configs/minigptv2_benchmark_evaluation.yaml
dataset names:
okvqa | vizwiz | iconvqa | gqa | vsr | hm |
---|
torchrun --master-port ${port} --nproc_per_node 1 eval_vqa.py \
--cfg-path ${cfg_path} --dataset okvqa,vizwiz,iconvqa,gqa,vsr,hm