Update README.md

README.md

@@ -32,27 +32,107 @@ The text in the dataset is all in English.
 ### Four Subsets
 
 Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs.
+For all subsets, the validation and test splits remain the same. The validation split(1.08GB) contains 412 prompts and 3.2K images and 
+the test(1.14GB) split cotains 466 prompts and 3.4K images. The information of the train split in different scales is as following:
 
-|Subset|Num of Images|Num of Prompts|Size|Image Directory|
-|:--|--:|--:|--:|--:|
-|ImageRewardDB 1K|TBD|1K|TBD|`images/`|
-|ImageRewardDB 2K|TBD|2K|TBD|`images/`|
-|ImageRewardDB 4K|TBD|4K|TBD|`images/`|
-|ImageRewardDB 8K|TBD|8K|TBD|`images/`|
+|Subset|Num of Images|Num of Prompts|Size|
+|:--|--:|--:|--:|
+|ImageRewardDB 1K|7.8K|1K|2.7GB|
+|ImageRewardDB 2K|15.6K|2K|5.4GB|
+|ImageRewardDB 4K|31.4K|4K|10.6GB|
+|ImageRewardDB 8K|62.6K|8K|20.6GB|
 
 ## Dataset Structure
 
+All the data in this repository is stored in a well organized way. The 62.6K images in ImageRewardDB are split into several folders, 
+stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, its corresponding 
+images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation.
+The file structure is as following:
+```
+# ImageRewardDB
+./
+├── images
+│   ├── train
+│   │   ├── train_1
+│   │   │   ├── 0a1ed3a5-04f6-4a1b-aee6-d584e7c8ed9c.webp
+│   │   │   ├── 0a58cfa8-ff61-4d31-9757-27322aec3aaf.webp
+│   │   │   ├── [...]
+│   │   │   └── train_1.json
+│   │   ├── train_2
+│   │   ├── train_3
+│   │   ├── [...]
+│   │   └── train_32
+│   ├── validation
+│   │   └── [...]
+│   └── test
+│       └── [...]
+├── metadata-train.parquet
+├── metadata-validation.parquet
+└── metadata-test.parquet
+```
+The sub-folders have the name of <split_name>_<part_id>, and the JSON file have the same name as the sub-folder.
+Each image is a lossless WebP file, and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).
+
 ### Data Instances
 
-[More Information Needed]
+For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp` and its information in train_1.json.
+
+```json
+{
+  "image_path": "images/train/train_1/0280642d-f69f-41d1-8598-5a44e296aa8b.webp",
+  "prompt_id": "000864-0061",
+  "prompt": "painting of a holy woman, decorated, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k ",
+  "classification": "People",
+  "image_amount_in_total": 9,
+  "rank": 5,
+  "overall_rating": 4,
+  "image_text_alignment_rating": 3,
+  "fidelity_rating": 4
+}
+```
 
 ### Data Fields
 
-[More Information Needed]
+* image: The image object
+* prompt_id: The id of the corresponding prompt
+* prompt: The text of the corresponding prompt
+* classification: The classification of the corresponding prompt
+* image_amount_in_total: Total amount of images related to the prompt
+* rank: The relative rank of the image in all related images
+* overall_rating: The overall score of this image
+* image_text_alignment_rating: The score of how well the generated image matchs the given text
+* fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have
 
 ### Data Splits
 
-[More Information Needed]
+As we mentioned above, all scales of the subsets we provided have three spilts of "train", "validtion", and "test". 
+And all the subsets share the same validation and test splits.
+
+### Dataset Metadata
+
+We also include three metadata tables `metadata-train.parquet`, `metadata-validation.parquet`, and `metadata-test.parquet` to 
+help you access and comprehend ImageRewardDB without downloading the Zip files.
+
+All the tables share the same schema, and each row refers to an image. The schema is shown below, 
+and actually the JSON files we mentioned above share the same schema:
+
+|Column|Type|Description|
+|:---|:---|:---|
+|`image_path`|`string`|The relative path of the image in the repository.|
+|`prompt_id`|`string`|The id of the corresponding prompt.|
+|`prompt`|`string`|The text of the corresponding prompt.|
+|`classification`|`string`| The classification of the corresponding prompt.|
+|`image_amount_in_total`|`int`| Total amount of images related to the prompt.|
+|`rank`|`int`| The relative rank of the image in all related images.|
+|`overall_rating`|`int`| The overall score of this image.
+|`image_text_alignment_rating`|`int`|The score of how well the generated image matchs the given text.|
+|`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.|
+
+Below are an example row from metadata-train.parquet.
+
+|image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating|
+|:---|:---|:---|:---|:---|:---|:---|:---|:---|
+|images/train/train_1/1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp|001324-0093|a magical forest that separates the good world from the dark world, fantasy art by greg rutkowski, loish, rhads, ferdinand knab, makoto shinkai and lois van baarle, ilya kuvshinov, rossdraws, tom bagshaw, global illumination, radiant light, detailed and intricate environment|Outdoor Scenes|9|3|6|6|6|
 
 ## Dataset Creation