Datasetpublic

OmarK211/photo-test

Anime vs. Live Action Film Image Classification OmarK211/photo-test A binary image classification dataset designed to distinguish between Anime (Label 0) and Live Action (Label 1) film frames/imagery. Images are prepared as standardized square RGB files with multi-pass synthetic training variants. Source and task The images were manually imported from the web into my local computer. Then manually uploaded to Google Colab Preparation source: 24-679 Image Data… See the full description on the dataset page: https://huggingface.co/datasets/OmarK211/photo-test.

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Dataset Card

Anime vs. Live Action Film Image Classification

OmarK211/photo-test

A binary image classification dataset designed to distinguish between Anime (Label 0) and Live Action (Label 1) film frames/imagery. Images are prepared as standardized square RGB files with multi-pass synthetic training variants.

Source and task

The images were manually imported from the web into my local computer. Then manually uploaded to Google Colab

Preparation source: 24-679 Image Data notebook. Course: 24-679, Fall 2026, Carnegie Mellon University. Repository maintainer: the account shown above.

Fields

Field	Meaning and modeling role
`image`	Prepared RGB pixel array (224×224); model input image.
`label`	Target binary class: 0 = anime; 1 = live_action.
`label_name`	Human-readable category string ("anime" / "live_action").
`source_id`, `parent_id`	Unique sample ID and root image provenance ID.
`augmentation`, `is_augmented`	Applied image transform identifier and synthetic flag.

Splits and original-source counts

These counts are computed from the packaged splits for this execution run:

Split	Original rows	Synthetic rows	Total rows
train	22	352	374
validation	4	0	4
test	4	0	4

Include synthetic rows in training only.

Split	Label	Rows
train	0	187
train	1	187
validation	0	2
validation	1	2
test	0	2
test	1	2

The holdout split uses explicit balanced class stratification. Validation and test splits contain strictly unaugmented original images.

Augmentation and preprocessing

Prepared image resolution in this run: 224 × 224 RGB. Neutral canvas padding is (128, 128, 128).

All input files undergo standardized pipeline transformations: EXIF orientation alignment, RGB conversion, proportional scaling, and centering on a 224x224 canvas.

Training inputs include multi-pass synthetic variants generated via randomized brightness adjustments, canvas rotations, contrast scaling, and Gaussian blurring.

Training method	Stored rows
`mild_brightness`	88
`mild_contrast`	88
`mild_gaussian_blur`	88
`none`	22
`small_rotation`	88

Intended use and limitations

Designed for computer vision model training, transfer learning tasks, and dataset inspection. Synthetic samples expand training volume but do not replace independent real-world captures.

Load and compare

python

from datasets import load_dataset
ds = load_dataset("OmarK211/photo-test")
# Train with ds["train"], choose settings with ds["validation"], then score ds["test"].

Use an account with access if repository visibility changes. For reproducible comparisons, record the dataset commit and model/environment versions. Regenerate this card with the preparation notebook after changing the data; its counts are calculated from the actual packaged splits. The YAML schema and split configuration are preserved from the upload.