Datasets:
Tasks:
Image Classification
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
File size: 1,636 Bytes
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---
language:
- en
license: mit
tags:
- image-classification
- computer-vision
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
dataset_info:
features:
- name: image
dtype:
array3_d:
shape:
- 128
- 128
- 3
dtype: uint8
- name: label
dtype:
class_label:
names:
'0': cats
'1': dogs
splits:
- name: train
num_bytes: 921696000
num_examples: 8000
- name: test
num_bytes: 230424000
num_examples: 2000
download_size: 487392383
dataset_size: 1152120000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Cats and Dogs Image Classification Dataset
This dataset contains images of cats and dogs, intended for image classification tasks. It includes two classes: "cats" and "dogs".
## Dataset Structure
The dataset is structured into two splits:
* **train**: Contains 8000 images for training.
* **test**: Contains 2000 images for testing.
Images are stored in RGB format with a resolution of 128x128 pixels.
## Data Loading and Usage
The dataset can be loaded using the Hugging Face Datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("cats_dogs_dataset")
This will return a DatasetDict object with the train and test splits.
Example Usage
Python
from datasets import load_dataset
dataset = load_dataset("cats_dogs_dataset")
# Access the first training example
example = dataset["train"]
# Print the image and label
print(example["image"])
print(example["label"]) |