| | --- |
| | task_categories: |
| | - image-classification |
| |
|
| | --- |
| | # Dataset for project: Pet-Ray |
| |
|
| | ## Dataset Description |
| |
|
| | This G-Ray dataset has been processed by AutoTrain for Pet-Ray. |
| |
|
| | ### Languages |
| |
|
| | The BCP-47 code for the dataset's language is unk. |
| |
|
| | ## Dataset Structure |
| |
|
| | ### Data Instances |
| |
|
| | A sample from this dataset looks as follows: |
| |
|
| | ```json |
| | [ |
| | { |
| | "image": "<1800x4000 RGB PIL image>", |
| | "target": 0 |
| | }, |
| | { |
| | "image": "<1800x4000 RGB PIL image>", |
| | "target": 0 |
| | } |
| | ] |
| | ``` |
| |
|
| | ### Dataset Fields |
| |
|
| | The dataset has the following fields (also called "features"): |
| |
|
| | ```json |
| | { |
| | "image": "Image(decode=True, id=None)", |
| | "target": "ClassLabel(names=['chubs'], id=None)" |
| | } |
| | ``` |
| |
|
| | ### Dataset Splits |
| |
|
| | This dataset is split into a train and validation split. The split sizes are as follow: |
| |
|
| | | Split name | Num samples | |
| | | ------------ | ------------------- | |
| | | train | 11 | |
| | | valid | 3 | |
| |
|
