Sync folder via upload_folder API

.gitattributes

@@ -62,3 +62,5 @@ HPD/qwen/HPD_train_data_qwen.json filter=lfs diff=lfs merge=lfs -text
 HPD/qwen/HPD_train_data_qwen_rev.json filter=lfs diff=lfs merge=lfs -text
 HPD/train_data.json filter=lfs diff=lfs merge=lfs -text
 OpenAI-4o_t2i_human_preference/train_data.json filter=lfs diff=lfs merge=lfs -text
+EvalMuse/pointwise/train_data.json filter=lfs diff=lfs merge=lfs -text
+EvalMuse/qwen/evalmuse_pointwise_train_data_qwen.json filter=lfs diff=lfs merge=lfs -text

EvalMuse/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+pointwise/train_data.json filter=lfs diff=lfs merge=lfs -text
+qwen/evalmuse_pointwise_train_data_qwen.json filter=lfs diff=lfs merge=lfs -text

EvalMuse/README.md

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+---
+license: mit
+---
+
+# EvalMuse
+
+## Dataset Summary
+
+This dataset is derived from [EvalMuse](https://huggingface.co/datasets/DY-Evalab/EvalMuse) for our [UnifiedReward-7B](https://huggingface.co/CodeGoat24/UnifiedReward-7b) training.
+
+For further details, please refer to the following resources:
+- 📰 Paper: https://arxiv.org/pdf/2503.05236
+- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
+- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
+- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
+- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
+
+## Citation
+```
+@article{unifiedreward-think,
+  title={Unified multimodal chain-of-thought reward model through reinforcement fine-tuning},
+  author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Wang, Chunyu and Lu, Qinglin and Jin, Cheng and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:2505.03318},
+  year={2025}
+}
+
+@article{unifiedreward,
+  title={Unified reward model for multimodal understanding and generation},
+  author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:2503.05236},
+  year={2025}
+}
+```

EvalMuse/download_images.sh

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+#!/bin/bash
+URL_LIST="urls.txt"
+
+DOWNLOAD_DIR="./images"
+
+mkdir -p "$DOWNLOAD_DIR"
+
+while IFS= read -r url || [ -n "$url" ]; do
+    echo "Downloading: $url"
+    wget -P "$DOWNLOAD_DIR" "$url"
+done < "$URL_LIST"
+
+echo "All downloads completed."
+
+cat images.zip.part-* > images.zip
+unzip images.zip

EvalMuse/images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e700b1392774cd65b8ebf5202a206cb27fe29ea66f0284d1e8605d63959f9cf
+size 54513618570

EvalMuse/pointwise/train_data.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b7e952a66018e906ea5d082c41c58e38bd1ff4944cc56d50ff600df77649173
+size 45136389

EvalMuse/qwen/evalmuse_pointwise_train_data_qwen.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1942041b876dea55ce9a42677fa999df67258b34fd9e066b28f6d5f6cbdb5ec3
+size 46524047

EvalMuse/read.py

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+import pyarrow.parquet as pq
+from pathlib import Path
+import json
+from typing import List, Dict, Any, Optional
+
+def write_json(file_path: str, data: Any):
+    with open(file_path, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=4)
+        
+data_dir = Path("/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/EvalMuse/images")
+# out_dir = Path("/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/T2I-CoReBench-main/Bench/GEditBench-v2/Images")
+# out_dir.mkdir(parents=True, exist_ok=True)
+
+parquet_files = sorted(data_dir.glob("*.parquet"))
+
+total_saved = 0
+total_skipped = 0
+
+save_data = []
+
+idx = 1
+save_data = []
+
+for parquet_path in parquet_files:
+    print(f"\nreading {parquet_path}")
+
+    pf = pq.ParquetFile(parquet_path)
+
+    for rg in range(pf.num_row_groups):
+        table = pf.read_row_group(rg).combine_chunks()
+        rows = table.to_pylist()
+
+        for i, row in enumerate(rows):
+            prompt = row['prompt']
+            image1_score = row['weighted_results_image1_preference']
+            image2_score = row['weighted_results_image2_preference']
+            gpt_value = ""
+            if image1_score > image2_score:
+                gpt_value = "Image 1 is better than Image 2."
+                image1_path = f'images/{idx}_chosen.png'
+                image2_path = f"images/{idx}_rejected.png"
+            elif image2_score > image1_score:
+                gpt_value = "Image 2 is better than Image 1."
+                image2_path = f'images/{idx}_chosen.png'
+                image1_path = f"images/{idx}_rejected.png"
+            
+            print(row['image1'].keys())
+            image1_byte = row['image1']['bytes']
+            import os
+            with open(os.path.join("/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/OpenAI-4o_t2i_human_preference",image1_path), "wb") as f:
+                f.write(image1_byte)
+            
+            
+            image2_byte = row['image2']['bytes']
+            with open(os.path.join("/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/OpenAI-4o_t2i_human_preference",image2_path), "wb") as f:
+                f.write(image2_byte)
+            
+            template = {
+                "id": f"{idx}",
+                "prompt": f"{prompt}",
+                "conversations": [
+                    {
+                        "from": "human",
+                        "value": "<image>\n <image>\nYou are given a text caption and two generated images based on that caption. Your task is to evaluate and compare these images based on two key criteria:\n1. Alignment with the Caption: Assess how well each image aligns with the provided caption. Consider the accuracy of depicted objects, their relationships, and attributes as described in the caption.\n2. Overall Image Quality: Examine the visual quality of each image, including clarity, detail preservation, color accuracy, and overall aesthetic appeal.\nCompare both images using the above criteria and select the one that better aligns with the caption while exhibiting superior visual quality.\nProvide a clear conclusion such as \"Image 1 is better than Image 2.\", \"Image 2 is better than Image 1.\" and \"Both images are equally good.\".\nYour task is provided as follows:\nText Caption: [A harmoniously crafted glass sculpture, cinematically capturing the interplay of fire and ice, with dramatic lighting and deep, rich colors.]"
+                    },
+                    {
+                        "from": "gpt",
+                        "value": gpt_value
+                    }
+                ],
+                "images": [
+                    image1_path,
+                    image2_path
+                ]
+            }
+            idx += 1
+            save_data.append(template)
+            
+write_json("/inspire/qb-ilm/project/deepgen/wangdianyi-240107110022/BXH/RL_Data/OpenAI-4o_t2i_human_preference/train_data.json", save_data)