Update README.md

README.md

@@ -1,63 +1,225 @@
 ---
 configs:
-  - config_name: Absolute_depth
-    data_files:
-      - split: train
-        path: train/Absolute_depth/train-*.parquet
-  - config_name: Action
-    data_files:
-      - split: train
-        path: train/Action/train-*.parquet
-  - config_name: Color
-    data_files:
-      - split: train
-        path: train/Color/train-*.parquet
-  - config_name: Counting
-    data_files:
-      - split: train
-        path: train/Counting/train-*.parquet
-  - config_name: Emotion
-    data_files:
-      - split: train
-        path: train/Emotion/train-*.parquet
-  - config_name: Fine-grained
-    data_files:
-      - split: train
-        path: train/Fine-grained/train-*.parquet
-  - config_name: Localization
-    data_files:
-      - split: train
-        path: train/Localization/train-*.parquet
-  - config_name: OCR
-    data_files:
-      - split: train
-        path: train/OCR/train-*.parquet
-  - config_name: Orientation
-    data_files:
-      - split: train
-        path: train/Orientation/train-*.parquet
-  - config_name: Recognition
-    data_files:
-      - split: train
-        path: train/Recognition/train-*.parquet
-  - config_name: Relative_depth
-    data_files:
-      - split: train
-        path: train/Relative_depth/train-*.parquet
-  - config_name: Scene_Classification
-    data_files:
-      - split: train
-        path: train/Scene_Classification/train-*.parquet
-  - config_name: Spatial
-    data_files:
-      - split: train
-        path: train/Spatial/train-*.parquet
-  - config_name: Texture
-    data_files:
-      - split: train
-        path: train/Texture/train-*.parquet
+- config_name: Absolute_depth
+  data_files:
+  - split: train
+    path: train/Absolute_depth/train-*.parquet
+- config_name: Action
+  data_files:
+  - split: train
+    path: train/Action/train-*.parquet
+- config_name: Color
+  data_files:
+  - split: train
+    path: train/Color/train-*.parquet
+- config_name: Counting
+  data_files:
+  - split: train
+    path: train/Counting/train-*.parquet
+- config_name: Emotion
+  data_files:
+  - split: train
+    path: train/Emotion/train-*.parquet
+- config_name: Fine-grained
+  data_files:
+  - split: train
+    path: train/Fine-grained/train-*.parquet
+- config_name: Localization
+  data_files:
+  - split: train
+    path: train/Localization/train-*.parquet
+- config_name: OCR
+  data_files:
+  - split: train
+    path: train/OCR/train-*.parquet
+- config_name: Orientation
+  data_files:
+  - split: train
+    path: train/Orientation/train-*.parquet
+- config_name: Recognition
+  data_files:
+  - split: train
+    path: train/Recognition/train-*.parquet
+- config_name: Relative_depth
+  data_files:
+  - split: train
+    path: train/Relative_depth/train-*.parquet
+- config_name: Scene_Classification
+  data_files:
+  - split: train
+    path: train/Scene_Classification/train-*.parquet
+- config_name: Spatial
+  data_files:
+  - split: train
+    path: train/Spatial/train-*.parquet
+- config_name: Texture
+  data_files:
+  - split: train
+    path: train/Texture/train-*.parquet
+license: cc-by-4.0
+task_categories:
+- visual-question-answering
+- image-classification
+- image-to-text
+language:
+- en
+tags:
+- multimodal
+- vision-language
+- benchmark
+- instruction-tuning
+size_categories:
+- 100K<n<1M
 ---
 
 # AVA-Bench
 
-Each capability is a separate subset. Pick one in the viewer's config dropdown.
+Training dataset for the paper **AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models** ([arXiv:2506.09082](https://arxiv.org/abs/2506.09082)) accepted in **CVPR 2026**.
+
+AVA-Bench is a diagnostic benchmark for evaluating Vision Foundation Models (VFMs) through Atomic Visual Abilities (AVAs): fundamental perceptual skills such as localization, counting, OCR, spatial understanding, depth estimation, color recognition, texture recognition, and fine-grained recognition. 
+
+AVA-Bench disentangls visual perception into **14 atomic visual capabilities**, each with distribution-matched training and evaluation splits. This allows researchers to measure where a VFM excels or fails and to construct capability-level “ability fingerprints” for model comparison and selection. 
+
+This Hub release contains the **training split** of AVA-Bench. The evaluation split is released separately; see the project page and paper for details.
+
+## Capabilities
+
+AVA-Bench covers **14 atomic visual capabilities**, each released as its own subset/config:
+
+| Capability | Tests |
+|---|---|
+| `Action` | Recognizing human/animal actions in images |
+| `Color` | Identifying object colors |
+| `Counting` | Counting instances of an object |
+| `Emotion` | Recognizing emotion from facial expressions/scenes |
+| `Fine-grained` | Fine-grained category discrimination, such as bird, plant, animal, fungi, or aircraft categories |
+| `Localization` | Locating objects via bounding-box queries |
+| `OCR` | Reading text rendered in images |
+| `Orientation` | Determining the orientation or pose of objects |
+| `Recognition` | Object/entity recognition |
+| `Scene_Classification` | Classifying the overall scene/place |
+| `Spatial` | Reasoning about spatial relationships between objects |
+| `Texture` | Identifying surface textures |
+| `Absolute_depth` | Estimating absolute depth from a single image |
+| `Relative_depth` | Comparing depth between two regions |
+
+## Dataset structure
+
+Each subset has a single `train` split, stored as Parquet shards with image bytes **embedded** in the file.
+
+### Data fields
+
+Every example contains:
+
+- `image` (`datasets.Image`) — the input image, decoded as a PIL image on access.
+- `id` (`string`) — unique example identifier.
+- `conversations` (`list` of `{from, value}`) — instruction-tuning style turns. The `human` turn includes the question, usually with an `<image>` placeholder, and the `gpt` turn includes the ground-truth answer.
+
+Some capabilities may additionally include fields such as:
+
+- `height`
+- `width`
+- `category`
+- `area`
+- bounding-box or region metadata, depending on the capability
+Per-subset row counts are visible in the dataset viewer's config dropdown.
+
+## Usage
+Please go to github to use the dataset to evaluate Vision Foundation Models. If you want to check the dataset:
+
+```python
+from datasets import load_dataset
+
+# Load one capability
+ds = load_dataset("act13/AVA-Bench", name="Counting", split="train")
+
+print(ds[0])
+# {
+#   'id': '...',
+#   'image': <PIL.Image.Image image mode=RGB ...>,
+#   'conversations': [
+#       {'from': 'human', 'value': '<image>\n...'},
+#       {'from': 'gpt', 'value': '...'}
+#   ],
+#   ...
+# }
+```
+
+To stream without downloading the full subset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "act13/AVA-Bench",
+    name="Counting",
+    split="train",
+    streaming=True,
+)
+
+for ex in ds.take(5):
+    print(ex["conversations"][0]["value"])
+    print(ex["conversations"][1]["value"])
+```
+
+## Intended uses
+
+AVA-Bench is intended for research on vision foundation models and vision-language systems. Suitable uses include:
+
+- Training or instruction-tuning vision-language models on atomic visual abilities.
+- Diagnosing which visual capabilities a VFM lacks.
+- Comparing VFMs through capability-level performance rather than only aggregate VQA accuracy.
+- Constructing balanced training mixtures across visual abilities.
+- Studying how different VFM pretraining objectives affect downstream perceptual capabilities.
+
+
+
+## Source datasets
+
+AVA-Bench is curated from multiple existing datasets, depending on the atomic visual ability. Source datasets include, but are not necessarily limited to:
+
+- Objects365
+- LVIS
+- iNaturalist-2021
+- DIOR
+- NYU-Depth V2
+- KITTI
+- COCO-Text
+- IIIT5K
+- TextVQA
+- EgoOrientBench
+- CURE-OR
+- Places434
+- AID
+- CUB-200-2011
+- FGVC-Aircraft
+- MiT
+- DTD
+- Kylberg
+- KTH-TIPS
+- KTH-TIPS2
+
+Please see the paper for the full per-capability dataset construction details and source-license breakdown.
+
+## License
+
+This dataset card and the AVA-Bench organization/annotations are released under **CC-BY-4.0**.
+
+Underlying images retain the licenses of their original source datasets. Users are responsible for respecting the license terms and usage restrictions of each source dataset.
+
+## Citation
+
+If you use AVA-Bench, please cite:
+
+```bibtex
+@article{mai2025ava,
+  title={Ava-bench: Atomic visual ability benchmark for vision foundation models},
+  author={Mai, Zheda and Chowdhury, Arpita and Wang, Zihe and Jeon, Sooyoung and Wang, Lemeng and Hou, Jiacheng and Chao, Wei-Lun},
+  journal={arXiv preprint arXiv:2506.09082},
+  year={2025}
+}
+```
+
+## Contact
+
+Open a discussion on this dataset's Community tab, or reach the authors via the contact information provided in the paper.