Mitsua/color-multi-fractal-db-1k
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How to use Mitsua/swin-base-multi-fractal-1k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="Mitsua/swin-base-multi-fractal-1k")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png") # Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Mitsua/swin-base-multi-fractal-1k")
model = AutoModelForImageClassification.from_pretrained("Mitsua/swin-base-multi-fractal-1k", device_map="auto")Swin Transformer model pre-trained on Color Multi Fractal DB 1k (1 million images, 1k classes) at resolution 224x224 for 300 epochs, developed by ELAN MITSUA Project / Abstract Engine.
This model is trained exclusively on 1 million fractal images which relies solely on mathematical formulas, so no real images or pretrained models are used for this training.
The Swin Transformer is a type of Vision Transformer and can be utilized for various downstream tasks. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. and first released in this repository.