Image Feature Extraction
Transformers
ONNX
Safetensors
English
Japanese
egara_net
feature-extraction
embeddings
illustration
vision-transformer
dino
custom-architecture
custom_code
Instructions to use Columba1198/EgaraNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Columba1198/EgaraNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Columba1198/EgaraNet", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Columba1198/EgaraNet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
README.md
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| **Architecture** | DINOv3 ViT-L/16 backbone + StyleNet (Transposed Attention Transformer) head |
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| **Embedding Dim** | 1024 |
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| **Input** | RGB images, any resolution (
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| **Output** | L2-normalized style embedding vector |
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| **Training Data** | ~1.2M illustrations from ~12K artists |
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| **Architecture** | DINOv3 ViT-L/16 backbone + StyleNet (Transposed Attention Transformer) head |
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| **Embedding Dim** | 1024 |
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| **Input** | RGB images, any resolution (must be a multiple of 16) |
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| **Output** | L2-normalized style embedding vector |
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| **Training Data** | ~1.2M illustrations from ~12K artists |
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