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README.md

@@ -1,3 +1,184 @@
----
-license: apache-2.0
----
+---
+pretty_name: Outpainted for Image Cropping
+license: other
+license_name: research-only-source-license-dependent
+task_categories:
+- image-to-image
+- object-detection
+tags:
+- image
+- computer-vision
+- image-cropping
+- bounding-box
+- outpainting
+- inpainting
+- stable-diffusion
+- composition
+- imagefolder
+size_categories:
+- 10K<n<100K
+---
+
+# Outpainted for Image Cropping
+
+<p align="center">
+  <a href="./README.md">English</a> | <a href="./README_zh.md">涓枃</a>
+</p>
+
+## Dataset Overview
+
+This dataset contains a collection of images generated by **Stable Diffusion v2 Inpaint** through outpainting, along with bounding box annotations indicating the 鈥渙riginal image region鈥?within each outpainted image. The dataset is mainly intended for research tasks such as image cropping, original frame recovery, composition-aware cropping, and outpainting-aware visual understanding.
+
+Each sample contains:
+
+- An outpainted image;
+- `orig_bbox`: the location of the original image in the expanded canvas;
+- `composition_tags`: a list of image composition tags, some of which may be empty.
+
+## Data Generation Pipeline
+
+![pipeline_en](./assets/pipeline_en.png)
+
+The data generation pipeline is as follows:
+
+1. Collect professional photographs or high-aesthetic-score images.
+2. Obtain or generate image descriptions, for example by using BLIP to generate captions.
+3. Set the expansion margins.
+4. Use **Stable Diffusion v2 Inpaint** to complete the expanded regions.
+5. Use positive prompts to constrain the generated content.
+6. Use negative prompts to reduce undesired content, such as `frame`, `border`, `text`, `watermark`, etc.
+7. Perform artifact detection and consistency detection on the generated results.
+8. Conduct manual inspection.
+9. Keep the samples that pass quality control, and record the bbox of the original image region to form training pairs. 
+
+
+`orig_bbox` uses the following format:
+
+```text
+[x_min, y_min, x_max, y_max]
+```
+
+This bbox represents the position of the original image region in the outpainted canvas, rather than an object bounding box in object detection.
+
+## Data Sources
+
+The source images of this dataset come from or refer to the following public datasets/repositories:
+
+1. **PICD: Photographic Image Composition Dataset**  
+   https://github.com/CV-xueba/PICD_ImageComposition
+
+2. **LAION Aesthetics v2 4.75**  
+   https://huggingface.co/datasets/laion/aesthetics_v2_4.75
+
+3. **Landscape-Dataset**  
+   https://github.com/koishi70/Landscape-Dataset/tree/master
+
+
+## Dataset Structure
+
+```text
+outpainted-for-image-cropping/
+鈹溾攢鈹€ README.md
+鈹溾攢鈹€ metadata.jsonl
+鈹溾攢鈹€ stats.json
+鈹斺攢鈹€ images/
+    鈹溾攢鈹€ img_000000.png
+    鈹溾攢鈹€ img_000001.png
+    鈹斺攢鈹€ ...
+```
+
+Each line in `metadata.jsonl` corresponds to one sample, for example:
+
+```json
+{
+  "file_name": "images/img_000000.png",
+  "orig_bbox": [281, 77, 881, 487],
+  "composition_tags": ["HORI2"]
+}
+```
+
+### Field Description
+
+- `file_name`: the relative path of the outpainted image.
+- `orig_bbox`: the bounding box of the original image region in the outpainted canvas, in the format `[x_min, y_min, x_max, y_max]`.
+- `composition_tags`: a list of composition tags parsed from the original dataset. If there is no reliable composition tag, it is an empty list `[]`.
+
+## Dataset Statistics
+High-frequency composition tags:
+
+| Tag | Count |
+|---|---:|
+| HORI2 | 1,956 |
+| HORI3 | 1,694 |
+| DIFFUSE | 1,600 |
+| DENSE | 1,436 |
+| DIA | 1,305 |
+| LINE_VERTI3 | 1,156 |
+| PATTERN | 1,000 |
+| LINE_VERTI_MANY | 983 |
+| POINT_MULTI_HORI | 64 |
+| LINE_VERTI2 | 55 |
+
+## Usage
+
+Load from the Hugging Face Hub:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("zzsyppt/outpainted-for-image-cropping")
+print(dataset)
+print(dataset["train"][0])
+```
+
+Check locally before uploading:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("imagefolder", data_dir="./hf_dataset")
+print(dataset)
+print(dataset["train"][0])
+```
+
+Expected fields include:
+
+```text
+image
+orig_bbox
+composition_tags
+```
+
+## Citation
+
+This dataset is for personal use only. If you use this dataset, please cite the corresponding upstream datasets based on the actual source of the samples used.
+
+### PICD
+
+```bibtex
+@inproceedings{zhao2025can,
+  title={Can Machines Understand Composition? Dataset and Benchmark for Photographic Image Composition Embedding and Understanding},
+  author={Zhao, Zhaoran and Lu, Peng and Zhang, Anran and Li, Peipei and Li, Xia and Liu, Xuannan and Hu, Yang and Chen, Shiyi and Wang, Liwei and Guo, Wenhao},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={14411--14421},
+  year={2025}
+}
+```
+
+### LAION-Aesthetics
+
+Please refer to the official LAION page and the corresponding Hugging Face dataset page to cite the related work of LAION-Aesthetics / LAION-5B:
+
+- https://laion.ai/blog/laion-aesthetics/
+- https://huggingface.co/datasets/laion/aesthetics_v2_4.75
+
+### Landscape-Dataset
+
+Please refer to the original repository:
+
+- https://github.com/koishi70/Landscape-Dataset/tree/master
+
+## Acknowledgements
+
+The generation of this dataset used Stable Diffusion v2 Inpaint and referenced or used public image data sources. We thank the creators and maintainers of the upstream datasets, repositories, and models.
+

README_zh.md

@@ -0,0 +1,156 @@
+---
+pretty_name: Outpainted for Image Cropping
+license: other
+license_name: research-only-source-license-dependent
+task_categories:
+- image-to-image
+- object-detection
+tags:
+- image
+- computer-vision
+- image-cropping
+- bounding-box
+- outpainting
+- inpainting
+- stable-diffusion
+- composition
+- imagefolder
+size_categories:
+- 10K<n<100K
+---
+
+# Outpainted for Image Cropping
+
+<p align="center">
+  <a href="./README.md">English</a> | <a href="./README_zh.md">涓枃</a>
+</p>
+
+## 鏁版嵁闆嗙畝浠?
+鏈暟鎹泦鍖呭惈涓€鎵归€氳繃 **Stable Diffusion v2 Inpaint** 鎵╁浘寰楀埌鐨勫浘鍍忥紝浠ュ強姣忓紶鎵╁浘鍥惧儚涓€滃師濮嬪浘鍍忓尯鍩熲€濈殑杈圭晫妗嗘爣娉ㄣ€傛暟鎹泦涓昏闈㈠悜鍥惧儚瑁佸壀銆佸師濮嬬敾骞呮仮澶嶃€佹瀯鍥炬劅鐭ヨ鍓拰鎵╁浘鎰熺煡瑙嗚鐞嗚В绛夌爺绌朵换鍔°€?
+姣忎釜鏍锋湰鍖呭惈锛?
+- 涓€寮犳墿鍥惧悗鐨勫浘鐗囷紱
+- `orig_bbox`锛氬師鍥惧湪鎵╁睍鐢诲竷涓殑浣嶇疆锛?- `composition_tags`锛氬浘鐗囨瀯鍥炬爣绛惧垪琛紝閮ㄥ垎涓虹┖銆?
+## 鏁版嵁鐢熸垚娴佺▼
+
+![image-20260503132555963](./assets/image-20260503132555963.png)
+
+鏁版嵁鐢熸垚娴佺▼濡備笅锛?
+1. 鏀堕泦涓撲笟鎽勫奖鍥剧墖鎴栭珮缇庡璇勫垎鍥剧墖銆?2. 鑾峰彇鎴栫敓鎴愬浘鍍忔弿杩帮紝渚嬪浣跨敤 BLIP 鐢熸垚 caption銆?3. 璁剧疆寰呮墿灞曡竟璺濄€?4. 浣跨敤 **Stable Diffusion v2 Inpaint** 瀵规墿灞曞尯鍩熻繘琛岃ˉ鍏ㄣ€?5. 浣跨敤姝ｉ潰鎻愮ず璇嶇害鏉熺敓鎴愬唴瀹广€?6. 浣跨敤璐熼潰鎻愮ず璇嶅噺灏戜笉甯屾湜鍑虹幇鐨勫唴瀹癸紝渚嬪 `frame`銆乣border`銆乣text`銆乣watermark` 绛夈€?7. 瀵圭敓鎴愮粨鏋滆繘琛屼吉褰辨娴嬨€佷竴鑷存€ф娴嬨€?8. 杩涜浜哄伐鏍告煡銆?9. 淇濈暀閫氳繃璐ㄩ噺妫€鏌ョ殑鏍锋湰锛屽苟璁板綍鍘熷浘鍖哄煙 bbox锛屽舰鎴?training pairs銆?
+
+
+`orig_bbox` 浣跨敤濡備笅鏍煎紡锛?
+```text
+[x_min, y_min, x_max, y_max]
+```
+
+璇?bbox 琛ㄧず鍘熷鍥惧儚鍖哄煙鍦ㄦ墿鍥惧悗鐢诲竷涓殑浣嶇疆锛岃€屼笉鏄洰鏍囨娴嬩腑鐨勭墿浣撴銆?
+## 鏁版嵁鏉ユ簮
+
+鏈暟鎹泦鐨勬簮鍥惧儚鏉ヨ嚜鎴栧弬鑰冧互涓嬪叕寮€鏁版嵁闆?浠撳簱锛?
+1. **PICD: Photographic Image Composition Dataset**  
+   https://github.com/CV-xueba/PICD_ImageComposition
+
+2. **LAION Aesthetics v2 4.75**  
+   https://huggingface.co/datasets/laion/aesthetics_v2_4.75
+
+3. **Landscape-Dataset**  
+   https://github.com/koishi70/Landscape-Dataset/tree/master
+
+
+## 鏁版嵁缁撴瀯
+
+```text
+outpainted-for-image-cropping/
+鈹溾攢鈹€ README.md
+鈹溾攢鈹€ metadata.jsonl
+鈹溾攢鈹€ stats.json
+鈹斺攢鈹€ images/
+    鈹溾攢鈹€ img_000000.png
+    鈹溾攢鈹€ img_000001.png
+    鈹斺攢鈹€ ...
+```
+
+`metadata.jsonl` 涓瘡涓€琛屽搴斾竴涓牱鏈紝渚嬪锛?
+```json
+{
+  "file_name": "images/img_000000.png",
+  "orig_bbox": [281, 77, 881, 487],
+  "composition_tags": ["HORI2"]
+}
+```
+
+### 瀛楁璇存槑
+
+- `file_name`锛氭墿鍥惧悗鍥剧墖鐨勭浉瀵硅矾寰勩€?- `orig_bbox`锛氬師鍥惧尯鍩熷湪鎵╁浘鐢诲竷涓殑杈圭晫妗嗭紝鏍煎紡涓?`[x_min, y_min, x_max, y_max]`銆?- `composition_tags`锛氫粠鍘熷鏁版嵁闆嗕腑瑙ｆ瀽寰楀埌鐨勬瀯鍥炬爣绛惧垪琛ㄣ€傝嫢娌℃湁鍙潬鐨勬瀯鍥炬爣绛撅紝鍒欎负绌哄垪琛?`[]`銆?
+## 鏁版嵁闆嗙粺璁?楂橀鏋勫浘鏍囩锛?
+| 鏍囩 | 鏁伴噺 |
+|---|---:|
+| HORI2 | 1,956 |
+| HORI3 | 1,694 |
+| DIFFUSE | 1,600 |
+| DENSE | 1,436 |
+| DIA | 1,305 |
+| LINE_VERTI3 | 1,156 |
+| PATTERN | 1,000 |
+| LINE_VERTI_MANY | 983 |
+| POINT_MULTI_HORI | 64 |
+| LINE_VERTI2 | 55 |
+
+## 浣跨敤鏂瑰紡
+
+浠?Hugging Face Hub 鍔犺浇锛?
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("zzsyppt/outpainted-for-image-cropping")
+print(dataset)
+print(dataset["train"][0])
+```
+
+鏈湴涓婁紶鍓嶆鏌ワ細
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("imagefolder", data_dir="./hf_dataset")
+print(dataset)
+print(dataset["train"][0])
+```
+
+棰勬湡瀛楁鍖呮嫭锛?
+```text
+image
+orig_bbox
+composition_tags
+```
+
+## 寮曠敤
+
+鏈暟鎹泦浠呯敤浜庝釜浜虹敤閫斻€傚鏋滀娇鐢ㄦ湰鏁版嵁闆嗭紝璇锋牴鎹疄闄呬娇鐢ㄧ殑鏍锋湰鏉ユ簮寮曠敤瀵瑰簲涓婃父鏁版嵁闆嗐€?
+### PICD
+
+```bibtex
+@inproceedings{zhao2025can,
+  title={Can Machines Understand Composition? Dataset and Benchmark for Photographic Image Composition Embedding and Understanding},
+  author={Zhao, Zhaoran and Lu, Peng and Zhang, Anran and Li, Peipei and Li, Xia and Liu, Xuannan and Hu, Yang and Chen, Shiyi and Wang, Liwei and Guo, Wenhao},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={14411--14421},
+  year={2025}
+}
+```
+
+### LAION-Aesthetics
+
+璇峰弬鑰?LAION 瀹樻柟椤甸潰鍜屽搴?Hugging Face 鏁版嵁闆嗛〉闈㈠紩鐢?LAION-Aesthetics / LAION-5B 鐩稿叧宸ヤ綔锛?
+- https://laion.ai/blog/laion-aesthetics/
+- https://huggingface.co/datasets/laion/aesthetics_v2_4.75
+
+### Landscape-Dataset
+
+璇峰弬鑰冨師濮嬩粨搴擄細
+
+- https://github.com/koishi70/Landscape-Dataset/tree/master
+
+## 鑷磋阿
+
+鏈暟鎹泦鐨勭敓鎴愪娇鐢ㄤ簡 Stable Diffusion v2 Inpaint锛屽苟鍙傝€冩垨浣跨敤浜嗗叕寮€鍥惧儚鏁版嵁婧愩€傛劅璋笂娓告暟鎹泦銆佷粨搴撳拰妯″瀷鐨勫垱寤鸿€呬笌缁存姢鑰呫€?

stats.json

@@ -0,0 +1,94 @@
+{
+  "num_rows": 17684,
+  "tag_counts": {
+    "HORI2": 1956,
+    "HORI3": 1694,
+    "DIFFUSE": 1600,
+    "DENSE": 1436,
+    "DIA": 1305,
+    "LINE_VERTI3": 1156,
+    "PATTERN": 1000,
+    "LINE_VERTI_MANY": 983,
+    "POINT_MULTI_HORI": 64,
+    "LINE_VERTI2": 55,
+    "POINT_SHAPE_CENT": 41,
+    "POINT_1_ROT": 37,
+    "SHAPE_VERTI_MID": 30,
+    "SPECIAL_O": 22,
+    "POINT_MULTI_TRI": 17,
+    "SCATTER": 15,
+    "SPECIAL_C": 14,
+    "POINT_MULTI_VERTI": 13,
+    "POINT_MULTI_DIA": 11,
+    "PERSPECTIVE": 9,
+    "SHAPE_VERTI_AVERAGE": 7,
+    "SPECIAL_TRIANGLE": 4,
+    "SHAPE_VERTI_ONESIDE": 3
+  },
+  "tag_combo_counts": {
+    "<EMPTY>": 6589,
+    "HORI2": 1826,
+    "DIFFUSE": 1598,
+    "HORI3": 1566,
+    "DENSE": 1422,
+    "DIA": 1278,
+    "LINE_VERTI3": 1077,
+    "LINE_VERTI_MANY": 980,
+    "PATTERN": 972,
+    "LINE_VERTI3+HORI3": 47,
+    "POINT_MULTI_HORI+HORI2": 30,
+    "LINE_VERTI3+HORI2": 28,
+    "POINT_MULTI_HORI+HORI3": 25,
+    "LINE_VERTI2+HORI2": 25,
+    "SHAPE_VERTI_MID+LINE_VERTI2": 23,
+    "POINT_1_ROT+HORI3": 18,
+    "POINT_SHAPE_CENT+HORI2": 16,
+    "POINT_MULTI_TRI+SPECIAL_O": 15,
+    "POINT_1_ROT+HORI2": 14,
+    "POINT_SHAPE_CENT+HORI3": 14,
+    "DENSE+PATTERN": 10,
+    "HORI3+HORI2": 10,
+    "POINT_SHAPE_CENT+DIA": 8,
+    "SCATTER+HORI3": 7,
+    "POINT_MULTI_VERTI+POINT_MULTI_DIA": 7,
+    "PATTERN+DIA": 7,
+    "PERSPECTIVE+SPECIAL_C": 6,
+    "SHAPE_VERTI_AVERAGE+POINT_MULTI_HORI": 5,
+    "SPECIAL_TRIANGLE+PATTERN": 4,
+    "SCATTER+DIA": 4,
+    "LINE_VERTI2+PATTERN": 4,
+    "SHAPE_VERTI_MID+SPECIAL_O": 3,
+    "POINT_MULTI_HORI+POINT_MULTI_DIA": 3,
+    "POINT_1_ROT+DIA": 3,
+    "POINT_MULTI_VERTI+SPECIAL_O": 3,
+    "HORI3+SPECIAL_C": 3,
+    "POINT_1_ROT+LINE_VERTI3": 2,
+    "PERSPECTIVE+DIA": 2,
+    "SHAPE_VERTI_AVERAGE+LINE_VERTI_MANY": 2,
+    "DENSE+SHAPE_VERTI_MID": 2,
+    "SHAPE_VERTI_ONESIDE+HORI2": 2,
+    "POINT_MULTI_VERTI+LINE_VERTI2": 2,
+    "SCATTER+PATTERN": 1,
+    "POINT_MULTI_HORI+SPECIAL_C": 1,
+    "SHAPE_VERTI_ONESIDE+HORI3": 1,
+    "POINT_MULTI_DIA+HORI3": 1,
+    "LINE_VERTI3+SPECIAL_C": 1,
+    "POINT_MULTI_TRI+HORI2": 1,
+    "LINE_VERTI3+PATTERN": 1,
+    "DIA+SPECIAL_C": 1,
+    "DENSE+PERSPECTIVE": 1,
+    "HORI2+SPECIAL_C": 1,
+    "SHAPE_VERTI_MID+DIFFUSE": 1,
+    "SCATTER+HORI2": 1,
+    "POINT_SHAPE_CENT+DIFFUSE": 1,
+    "DENSE+DIA": 1,
+    "POINT_SHAPE_CENT+LINE_VERTI2+HORI2": 1,
+    "SHAPE_VERTI_MID+HORI3": 1,
+    "PATTERN+LINE_VERTI_MANY": 1,
+    "POINT_MULTI_TRI+HORI3": 1,
+    "SCATTER+SPECIAL_C": 1,
+    "POINT_MULTI_VERTI+DIA": 1,
+    "HORI2+POINT_SHAPE_CENT": 1,
+    "SCATTER+SPECIAL_O": 1
+  }
+}