Skip to content

README.md

Latest commit

 

History

History
101 lines (89 loc) · 6.47 KB

README.md

File metadata and controls

101 lines (89 loc) · 6.47 KB
Raw

Evaluations

We currently support evaluation for three main kinds of tasks:

  • VQA (Comprehension)
  • NLP (Comprehension)
  • Text2Image (Creation)

VQA Comprehension

We have supported the evaluation on several datasets at omni/eval/vqa.

Data Preparation

All formatted annotation files omni_comprehension_eval_format_files can be downloaded from Google Drive, the formatted MM-Vet dataset MM-VET can be downloaded from Google Drive. Please download them and put them under data.

Supported Datasets

Dataset Task Metrics Evaluation Server
VQAv2 VQA Accuarcy Server
OKVQA VQA Accuarcy Local
VizWiz VQA Accuarcy Server
TextVQA VQA Accuarcy Local
MM-Vet VQA Accuarcy Server
MMBench VQA Accuarcy Server
COCO Caption Captioning CIDEr Local
Image2Paragraph Captioning CIDEr Local
NoCaps Captioning CIDEr Local
DocVQA VQA ANLS Server
InfographicVQA VQA ANLS Server
POPE Hallucination Accuracy Local

Notes: DocVQA and InfographicVQA require high resolution to get a reasonable result, so a model that is trained on low-resolution images (e.g., 224x224) and uses CLIP as the vision encoder will get a very low performance. Models like Vary that use high-resolution images and hybrid image representations will be better at this task.

Run

To evaluate VQA tasks such as MM-Vet, please run the following:

# MM-Vet (Submit to https://huggingface.co/spaces/whyu/MM-Vet_Evaluator)
python -m omni.eval.vqa.eval_dreamllm \
    --model_name path2model \
    --gtfile_path ./data/MM-VET/MMGPT_mm-vet.json \
    --image_path ./data/MM-VET/images \
    --out_path ./samples/mm-vet \
    --num-chunks 1 \
    --datatype mmvet \
    --img_aug none \
    --beamsearch True \
    --evaltype "all" \
    --prompt "Please provide an accurate and detailed answer." \
    --system_prompt "This is an exam, please answer according to the image and question."

Then, submit the file results_final.json to server for the results.

We have provided a script, scripts/eval/vqa/eval_vqa.sh for testing different benchmarks.

NLP Comprehension

We have supported NLP evaluation on multi-task language processing and other QA datasets at omni/eval/language_eval.

Data Preparation

All formatted annotation files can be downloaded from Google Drive. The MMLU dataset can be downloaded from Google Drive.

Supported Datasets

Dataset Task Evaluation Server
BoolQ QA Local
PIQA QA Local
SIQA QA Local
HellaSwag QA Server
WinoGrande QA Local
MMLU Multi-task Local

Run

We have integrated a comprehensive evaluation toolkit called llama_evlaution_main. This toolkit supports various dataset evaluations with huggingface, but the dataset split may be different from the official ones that are typically used in papers. For official comparison, you can run the evaluation scripts at omni/eval/language_eval/submission_scripts. For example, if you want to evlaute BoolQ accuracy, please run:

python omni/eval/language_eval/submission_scripts/submission_dev_boolq.py \
    --model_dir path2model

Text2Image

We have supported text-to-image evaluation on COCO and LN-COCO at omni/eval/text2img.

Data Preparation

  • You have to first prepare the MS COCO images or the FID statistics files. The caption annotation files include captions_train2014.json and captions_val2014.json for MS COCO and lncoco_captions_val2017.jsonl for LN COCO. To calculate FID, you have to prepare the fid_stats.npz file, which is fid_stats_mscoco256_val.npz for MS COCO and fid_stats_lncoco256_val5k.npz for LN COCO. We have uploaded all these files in coco_fid_files on Goolge Drive.
  • If you have your own dataset, you can make the fid_stats file by running
python ./third_party/pytorch-fid/src/pytorch_fid/fid_score.py \
    --path "path2images" "path2fid_stats.npz" \
    --resolution 256 \
    --batch-size 50 \
    --save-stats

Supported Datasets

We currently support MS COCO and LN COCO datasets.

Dataset Task Metrics Evaluation Server
MS COCO Text2Image FID Local
LN COCO Text2Image FID Local

Run

We have provided the scripts to run COCO FID evaluation based on 8-times selection with CLIP. Just run:

OUT_DIR="OUTPUT_DIR"
MODEL_NAME_OR_PATH="YOUR_MODEL_PATH"
sh scripts/eval/text2img/eval_coco_zero_shot_clip8_select.sh $OUTPUT_DIR $MODEL_NAME_OR_PATH