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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.

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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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 / .json file containing filename and labels fields

Example (.csv):

csv
filename,label
portrait_001.jpg,"[0, 3]"
portrait_002.jpg,"[2]"
portrait_003.jpg,"[1, 2]"