Update README.md

README.md

@@ -121,15 +121,23 @@ language:
 pretty_name: PCA-Bench
 ---
 
+
 <h1 align="center">PCA-Bench</h1>
 
 <p align="center">
 
 <a href="https://github.com/pkunlp-icler/PCA-EVAL">
 <img alt="Static Badge" src="https://img.shields.io/badge/Github-Online-white">
-</a><a href="https://arxiv.org/abs/2310.02071">
-<img alt="Static Badge" src="https://img.shields.io/badge/Paper-PCAEVAL-red">
-<a href="https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1"><img alt="Static Badge" src="https://img.shields.io/badge/Datasets-HuggingFace-yellow">
+
+<a href="https://github.com/pkunlp-icler/PCA-EVAL/blob/main/PCA_Bench_Paper.pdf">
+<img alt="Static Badge" src="https://img.shields.io/badge/Paper-PCABench-red">
+
+<a href="https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1">
+<img alt="Static Badge" src="https://img.shields.io/badge/HFDataset-PCABenchV1-yellow">
+</a>
+
+<a href="https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV">
+<img alt="Static Badge" src="https://img.shields.io/badge/Leaderboard-Online-blue">
 </a>
 </p>
 
@@ -139,28 +147,75 @@ pretty_name: PCA-Bench
 *PCA-Bench is an innovative benchmark for evaluating and locating errors in Multimodal LLMs when conducting embodied decision making tasks, specifically focusing on perception, cognition, and action.*
 
 
-## News
-- PCA-Bench-V1 is released in HuggingFace Datasets (Leaderboard Coming Soon).
+## Release
+- [2024.02.15] [PCA-Bench-V1](https://github.com/pkunlp-icler/PCA-EVAL) is released. We release the open and closed track data in [huggingface](https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1). We also set an online [leaderboard ](https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV) accepting users' submission.
+- [2023.12.15] [PCA-EVAL](https://arxiv.org/abs/2310.02071) is accepted to Foundation Model for Decision Making Workshop @NeurIPS 2023. PCA-Evaluation tool is released in github.
 
+## Leaderboard
+[Leaderboard with Full Metrics](https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV)
 
 
-## Run Evaluation on Accuracy
 
+## Submit Results
+
+📢 For close track evaluaiton and PCA-Evaluation, please follow [this file](https://github.com/pkunlp-icler/PCA-EVAL/blob/main/pca-eval/results/chatgpt_holmes_outputs/Autonomous%20Driving.json) to organize your model output. Submit **Six JSON files** from different domains and different tracks, along with your **model name** and **organization** to us via [email](mailto:leo.liang.chen@stu.pku.edu.cn). Ensure you use the dataset's provided prompt as the default input for fair comparison.
+
+We will send the PCA-Eval results of your model to you and update the leaderboard.
+
+We provide sample code to get the six json files. User only needs to add your model inference code:
 ```python
-#pip install datasets
+# Sample code for PCA-Eval
 from datasets import load_dataset
+from tqdm import tqdm
+import json
+import os
+
+def YOUR_INFERENCE_CODE(prompt,image):
+    """Simple single round multimodal conversation call.
+    """
+    response = YOUR_MODEL.inference(prompt,image)
+    return response
+
+output_path = "./Results-DIR-PATH/"
+os.mkdir(output_path)
+
 dataset_ad = load_dataset("PCA-Bench/PCA-Bench-V1","Autonomous Driving")
 dataset_dr = load_dataset("PCA-Bench/PCA-Bench-V1","Domestic Robot")
 dataset_og = load_dataset("PCA-Bench/PCA-Bench-V1","Open-World Game")
 
-# use your model to inference on the test_open/close split with "question_prompt" and "image" given in the datasets and extract the answers.
-# compute the acc regarding the action groundtruth of open track. 
+test_dataset_dict = {"Autonomous-Driving":dataset_ad,"Domestic-Robot":dataset_dr,"Open-World-Game":dataset_og}
+test_split = ["test_closed","test_open"]
+test_domain = list(test_dataset_dict.keys())
+
+for domain in test_domain:
+  for split in test_split:
+    print("testing on %s:%s"%(domain,split))
+
+    prediction_results = []
+    output_filename = output_path+"%s-%s.json"%(domain,split)
+    prompts = test_dataset_dict[domain][split]['question_prompt']
+    images = test_dataset_dict[domain][split]['image']
+
+    for prompt_id in tqdm(range(len(prompts))):
+        user_inputs = prompts[prompt_id] # do not change the prompts for fair comparison
+        index = prompt_id
+        image = images[prompt_id]
+
+        outputs = YOUR_INFERENCE_CODE(user_inputs,image)
+
+        prediction_results.append({
+            'prompt': user_inputs,
+            'model_output': outputs,
+            'index': index,
+        })
+
+    with open(output_filename, 'w') as f:
+        json.dump(prediction_results, f, indent=4)
 
+# submit the 6 json files in the output_path to our email
 ```
 
-📢 For close track data, please follow [this file](https://github.com/pkunlp-icler/PCA-EVAL/blob/main/pca-eval/results/chatgpt_holmes_outputs/Autonomous%20Driving.json) to organize your model output. Submit **three JSON files** from different domains, along with your **model name** and **organization** to us via [email](mailto:leo.liang.chen@stu.pku.edu.cn). Ensure you use the dataset's provided prompt as the default input for fair comparison (You should mention in the email if custom prompts were used).
+You could also simply compute the multiple-choice accuracy locally as a comparison metric in your own experiments. However, in the online leaderboard, we only consider the average action score and Genuine PCA score when ranking models.
 
-We will send the closed track PCA-Eval results of your model to you.
 
-## PCA Evaluation
-To run PCA-Evaluation yourself, please follow the guidelines in the github repo [PCA-EVAL](https://github.com/pkunlp-icler/PCA-EVAL).
+For more information, refer to the offical [github repo](https://github.com/pkunlp-icler/PCA-EVAL)