Update README.md

README.md

@@ -56,24 +56,61 @@ We found that OCR and text-based processing become ineffective in VCR as accurat
 - **Paper:** [VCR: Visual Caption Restoration](https://arxiv.org/abs/2406.06462)
 - **Point of Contact:** [Tianyu Zhang](mailto:tianyu.zhang@mila.quebec)
 
-## Evaluation
+# Model Evaluation
+
+## Method 1 (recommended): use the evaluation script
+### Open-source evaluation
+We support open-source model_id: 
+```python
+["openbmb/MiniCPM-Llama3-V-2_5",
+"OpenGVLab/InternVL-Chat-V1-5",
+"internlm/internlm-xcomposer2-vl-7b",
+"HuggingFaceM4/idefics2-8b",
+"Qwen/Qwen-VL-Chat",
+"THUDM/cogvlm2-llama3-chinese-chat-19B",
+"THUDM/cogvlm2-llama3-chat-19B",
+"echo840/Monkey-Chat",]
+```
+For the models not on list, they are not intergated with huggingface, please refer to their github repo to create the evaluation pipeline.
 
-We recommend you to evaluate your model with [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). Before evaluating, please refer to the doc of `lmms-eval`.
+```bash
+# We use HuggingFaceM4/idefics2-8b and vcr_wiki_en_easy as an example
+# Inference from the VLMs and save the results to {model_id}_{difficulty}_{language}.json
+cd src/evaluation
+python3 inference.py --dataset_handler "vcr-org/VCR-wiki-en-easy-test" --model_id "HuggingFaceM4/idefics2-8b" --device "cuda" --dtype "bf16" --save_interval 50 --resume True
 
-```console
-pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+# Evaluate the results and save the evaluation metrics to {model_id}_{difficulty}_{language}_evaluation_result.json
+python3 evaluation_metrics.py --model_id HuggingFaceM4/idefics2-8b --output_path . --json_filename "HuggingFaceM4_idefics2-8b_en_easy.json" --dataset_handler "vcr-org/VCR-wiki-en-easy-test"
+
+# To get the mean score of all the `{model_id}_{difficulty}_{language}_evaluation_result.json` in `jsons_path` (and the std, confidence interval if `--bootstrap`) of the evaluation metrics
+python3 gather_results.py --jsons_path .
+```
+
+### Close-source evaluation
+We provide the evaluation script for the close-source model: `GPT-4o`, `GPT-4-Turbo`, `Claude-3-Opus` in the `evaluation` folder.
+
+You need an API Key, a pre-saved testing dataset and specify the path of the data saving the paper
+```bash
+cd src/evaluation
+# save the testing dataset to the path
+python3 save_image_from_dataset.py --output_path .
 
-# We use MiniCPM-Llama3-V-2_5 and vcr_wiki_en_easy as an example
-python3 -m accelerate.commands.launch \
-    --num_processes=8 \
-    -m lmms_eval \
-    --model minicpm_v \
-    --model_args pretrained="openbmb/MiniCPM-Llama3-V-2_5" \
-    --tasks vcr_wiki_en_easy \
-    --batch_size 1 \
-    --log_samples \
-    --log_samples_suffix MiniCPM-Llama3-V-2_5_vcr_wiki_en_easy \
-    --output_path ./logs/
+# Inference Put your API key and Image Path in the evaluation script (e.g. gpt-4o.py)
+python3 gpt-4o.py
+
+# Evaluate the results and save the evaluation metrics to {model_id}_{difficulty}_{language}_evaluation_result.json
+python3 evaluation_metrics.py --model_id gpt4o --output_path . --json_filename "gpt4o_en_easy.json" --dataset_handler "vcr-org/VCR-wiki-en-easy-test"
+
+# To get the mean score of all the `{model_id}_{difficulty}_{language}_evaluation_result.json` in `jsons_path` (and the std, confidence interval if `--bootstrap`) of the evaluation metrics
+python3 gather_results.py --jsons_path .
+```
+
+## Method 2: use lmms-eval framework
+You may need to incorporate the inference method of your model if the lmms-eval framework does not support it. For details, please refer to [here](https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/docs/model_guide.md)
+```bash
+pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+# We use HuggingFaceM4/idefics2-8b and vcr_wiki_en_easy as an example
+python3 -m accelerate.commands.launch --num_processes=8 -m lmms_eval --model idefics2 --model_args pretrained="HuggingFaceM4/idefics2-8b" --tasks vcr_wiki_en_easy --batch_size 1 --log_samples --log_samples_suffix HuggingFaceM4_idefics2-8b_vcr_wiki_en_easy --output_path ./logs/
 ```
 
 `lmms-eval` supports the following VCR `--tasks` settings: