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 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

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.