# AutoTrain Dataset for project: imagetest

## Dataset Description

This dataset has been automatically processed by AutoTrain for project imagetest.

### 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": "<32x32 RGB PIL image>",
    "feat_fine_label": 19,
    "target": 11
  },
  {
    "image": "<32x32 RGB PIL image>",
    "feat_fine_label": 29,
    "target": 15
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "image": "Image(decode=True, id=None)",
  "feat_fine_label": "ClassLabel(names=['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'cra', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'], id=None)",
  "target": "ClassLabel(names=['aquatic_mammals', 'fish', 'flowers', 'food_containers', 'fruit_and_vegetables', 'household_electrical_devices', 'household_furniture', 'insects', 'large_carnivores', 'large_man-made_outdoor_things', 'large_natural_outdoor_scenes', 'large_omnivores_and_herbivores', 'medium_mammals', 'non-insect_invertebrates', 'people', 'reptiles', 'small_mammals', 'trees', 'vehicles_1', 'vehicles_2'], 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        | 50000 |
| valid        | 10000 |

task_categories:image-classification region:us
{"task_categories": ["image-classification"]}
TAGS
#task_categories-image-classification #region-us 

AutoTrain Dataset for project: imagetest
========================================


Dataset Description
-------------------


This dataset has been automatically processed by AutoTrain for project imagetest.


### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
-----------------


### Data Instances


A sample from this dataset looks as follows:


### Dataset Fields


The dataset has the following fields (also called "features"):


### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:




### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
TAGS
#task_categories-image-classification #region-us 
 ### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
passage: TAGS
#task_categories-image-classification #region-us 
### Languages


The BCP-47 code for the dataset's language is unk.


Dataset Structure
-----------------### Data Instances


A sample from this dataset looks as follows:### Dataset Fields


The dataset has the following fields (also called "features"):### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow: