prithivMLmods/Multilabel-Portrait-18K
Multilabel-Portrait-18K Multilabel-Portrait-18K is a multi-label portrait classification dataset designed to analyze and categorize different styles of portrait images. It supports classification into the following four portrait types: 0 — Anime Portrait 1 — Cartoon Portrait 2 — Real Portrait 3 — Sketch Portrait This dataset is ideal for training and evaluating machine learning models in the domain of portrait-style classification. The goal is to enable accurate… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Multilabel-Portrait-18K.

Multilabel-Portrait-18K
Multilabel-Portrait-18K is a multi-label portrait classification dataset designed to analyze and categorize different styles of portrait images. It supports classification into the following four portrait types:
- 0 — Anime Portrait
- 1 — Cartoon Portrait
- 2 — Real Portrait
- 3 — Sketch Portrait
This dataset is ideal for training and evaluating machine learning models in the domain of portrait-style classification. The goal is to enable accurate recognition of artistic and real-world portraits for applications such as image generation, enhancement, style transfer, and content moderation.
Use Cases
- Multi-label classification for style recognition
- Pretraining or fine-tuning portrait classifiers
- Improving filters and sorting in creative AI applications
- Enhancing deepfake detection via portrait-style understanding
- Style-transfer or portrait enhancement tools
Dataset Details
- Total Samples: 18,000 portrait images
- Labels: Multi-label format (each image may have more than one label)
- Label Schema:
0: Anime Portrait [4,444]1: Cartoon Portrait [4,444]2: Real Portrait [4,444]3: Sketch Portrait [4,444]
Format
The dataset is typically provided in either:
- A directory structure grouped by label
- Or a
.csv/.jsonfile containingfilenameandlabelsfields
Example (.csv):
filename,label
portrait_001.jpg,"[0, 3]"
portrait_002.jpg,"[2]"
portrait_003.jpg,"[1, 2]"