.

README.md

@@ -73,12 +73,14 @@ EM means `"Exact Match"` and Jaccard means `"Jaccard Similarity"`. The best in c
 | GPT-4 Turbo | - | *78.74* | *88.54* | *45.15* | *65.72* | 0.2 | 8.42 | 0.0 | *8.58* |
 | GPT-4V | - | 52.04 | 65.36 | 25.83 | 44.63 | - | - | - | - |
 | GPT-4o | - | **91.55** | **96.44** | **73.2** | **86.17** | **14.87** | **39.05** | **2.2** | **22.72** |
+| GPT-4o-mini | - | 83.60 | 87.77 | 54.04 | 73.09 | 1.10 | 5.03 | 0 | 2.02 |
 | Gemini 1.5 Pro | - | 62.73 | 77.71 | 28.07 | 51.9 | 1.1 | 11.1 | 0.7 | 11.82 |
 | Qwen-VL-Max | - | 76.8 | 85.71 | 41.65 | 61.18 | *6.34* | *13.45* | *0.89* | 5.4 |
 | Reka Core | - | 66.46 | 84.23 | 6.71 | 25.84 | 0.0 | 3.43 | 0.0 | 3.35 |
 | Cambrian-1 | 34B | 79.69 | 89.27 | *27.20* | 50.04 | 0.03 | 1.27 | 0.00 | 1.37 |
 | Cambrian-1 | 13B | 49.35 | 65.11 | 8.37 | 29.12 | - | - | - | - |
 | Cambrian-1 | 8B | 71.13 | 83.68 | 13.78 | 35.78 | - | - | - | - |
+| CogVLM | 17B | 73.88 | 86.24 | 34.58 | 57.17 | - | - | - | - |
 | CogVLM2 | 19B | *83.25* | *89.75* | **37.98** | **59.99** | 9.15 | 17.12 | 0.08 | 3.67 |
 | CogVLM2-Chinese | 19B | 79.90 | 87.42 | 25.13 | 48.76 | **33.24** | **57.57** | **1.34** | **17.35** |
 | DeepSeek-VL | 1.3B | 23.04 | 46.84 | 0.16 | 11.89 | 0.0 | 6.56 | 0.0 | 6.46 |
@@ -89,9 +91,11 @@ EM means `"Exact Match"` and Jaccard means `"Jaccard Similarity"`. The best in c
 | InternLM-XComposer2-VL | 7B | 46.64 | 70.99 | 0.7 | 12.51 | 0.27 | 12.32 | 0.07 | 8.97 |
 | InternLM-XComposer2-VL-4KHD | 7B | 5.32 | 22.14 | 0.21 | 9.52 | 0.46 | 12.31 | 0.05 | 7.67 |
 | InternLM-XComposer2.5-VL | 7B | 41.35 | 63.04 | 0.93 | 13.82 | 0.46 | 12.97 | 0.11 | 10.95 |
-| InternVL-V1.5 | 25.5B | 14.65 | 51.42 | 1.99 | 16.73 | 4.78 | 26.43 | 0.03 | 8.46 |
+| InternVL-V1.5 | 26B | 14.65 | 51.42 | 1.99 | 16.73 | 4.78 | 26.43 | 0.03 | 8.46 |
 | InternVL-V2 | 26B | 74.51 | 86.74 | 6.18 | 24.52 | 9.02 | 32.50 | 0.05 | 9.49 |
 | InternVL-V2 | 40B | **84.67** | **92.64** | 13.10 | 33.64 | 22.09 | 47.62 | 0.48 | 12.57 |
+| InternVL-V2 | 76B | 83.20 | 91.26 | 18.45 | 41.16 | 20.58 | 44.59 | 0.56 | 15.31 |
+| InternVL-V2-Pro | - | 77.41 | 86.59 | 12.94 | 35.01 | 19.58 | 43.98 | 0.84 | 13.97 |
 | MiniCPM-V2.5 | 8B | 31.81 | 53.24 | 1.41 | 11.94 | 4.1 | 18.03 | 0.09 | 7.39 |
 | Monkey | 7B | 50.66 | 67.6 | 1.96 | 14.02 | 0.62 | 8.34 | 0.12 | 6.36 |
 | Qwen-VL | 7B | 49.71 | 69.94 | 2.0 | 15.04 | 0.04 | 1.5 | 0.01 | 1.17 |
@@ -100,39 +104,43 @@ EM means `"Exact Match"` and Jaccard means `"Jaccard Similarity"`. The best in c
 
 # Model Evaluation
 
-## Method 1 (recommended): use the evaluation script
-```bash
-git clone https://github.com/tianyu-z/VCR.git
-```
+## Method 1: 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",
+"internlm/internlm-xcomposer2-4khd-7b",
+"internlm/internlm-xcomposer2d5-7b",
 "HuggingFaceM4/idefics2-8b",
 "Qwen/Qwen-VL-Chat",
 "THUDM/cogvlm2-llama3-chinese-chat-19B",
 "THUDM/cogvlm2-llama3-chat-19B",
-"echo840/Monkey-Chat",]
+"THUDM/cogvlm-chat-hf",
+"echo840/Monkey-Chat",
+"THUDM/glm-4v-9b",
+"nyu-visionx/cambrian-phi3-3b",
+"nyu-visionx/cambrian-8b",
+"nyu-visionx/cambrian-13b",
+"nyu-visionx/cambrian-34b",
+"OpenGVLab/InternVL2-26B",
+"OpenGVLab/InternVL2-40B"
+"OpenGVLab/InternVL2-Llama3-76B",]
 ```
 For the models not on list, they are not intergated with huggingface, please refer to their github repo to create the evaluation pipeline. Examples of the inference logic are in `src/evaluation/inference.py`
 
 ```bash
 pip install -r requirements.txt
 # 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
-
 # 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 .
+python3 evaluation_pipeline.py --dataset_handler "vcr-org/VCR-wiki-en-easy-test" --model_id HuggingFaceM4/idefics2-8b --device "cuda" --output_path . --bootstrap --end_index 5000
 ```
+For large models like "OpenGVLab/InternVL2-Llama3-76B", you may have to use multi-GPU to do the evaluation. You can specify --device to None to use all GPUs available.
+
 
-### Close-source evaluation
+### Close-source evaluation (using API)
 We provide the evaluation script for the close-source models in `src/evaluation/closed_source_eval.py`.
 
 You need an API Key, a pre-saved testing dataset and specify the path of the data saving the paper
@@ -154,14 +162,46 @@ python3 evaluation_metrics.py --model_id gpt4o --output_path . --json_filename "
 # 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 [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) framework
+You may need to incorporate the inference method of your model if the VLMEvalKit framework does not support it. For details, please refer to [here](https://github.com/open-compass/VLMEvalKit/blob/main/docs/en/Development.md)
+```bash
+git clone https://github.com/open-compass/VLMEvalKit.git
+cd VLMEvalKit
+# We use HuggingFaceM4/idefics2-8b and VCR_EN_EASY_ALL as an example
+python run.py --data VCR_EN_EASY_ALL --model idefics2_8b --verbose
+```
+You may find the supported model list [here](https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/config.py).
 
-## Method 2: use lmms-eval framework
+`VLMEvalKit` supports the following VCR `--data` settings:
+
+* English
+  * Easy
+    * `VCR_EN_EASY_ALL` (full test set, 5000 instances)
+    * `VCR_EN_EASY_500` (first 500 instances in the VCR_EN_EASY_ALL setting)
+    * `VCR_EN_EASY_100` (first 100 instances in the VCR_EN_EASY_ALL setting)
+  * Hard
+    * `VCR_EN_HARD_ALL` (full test set, 5000 instances)
+    * `VCR_EN_HARD_500` (first 500 instances in the VCR_EN_HARD_ALL setting)
+    * `VCR_EN_HARD_100` (first 100 instances in the VCR_EN_HARD_ALL setting)
+* Chinese
+  * Easy
+    * `VCR_ZH_EASY_ALL` (full test set, 5000 instances)
+    * `VCR_ZH_EASY_500` (first 500 instances in the VCR_ZH_EASY_ALL setting)
+    * `VCR_ZH_EASY_100` (first 100 instances in the VCR_ZH_EASY_ALL setting)
+  * Hard
+    * `VCR_ZH_HARD_ALL` (full test set, 5000 instances)
+    * `VCR_ZH_HARD_500` (first 500 instances in the VCR_ZH_HARD_ALL setting)
+    * `VCR_ZH_HARD_100` (first 100 instances in the VCR_ZH_HARD_ALL setting)
+
+## Method 3: 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/
 ```
+You may find the supported model list [here](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main/lmms_eval/models).
+
 `lmms-eval` supports the following VCR `--tasks` settings:
 
 * English