# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/galaxy_type_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/galaxy_type_image_detection")Quick Links
Achieved 78% weighted accuracy for classification between 3 common galaxy types (S, E, Sb).
See my Kaggle notebook for more details.
Classification report:
precision recall f1-score support
E 0.7656 0.8848 0.8209 13592
S 0.7526 0.6685 0.7081 13591
SB 0.8262 0.7900 0.8077 13591
accuracy 0.7811 40774
macro avg 0.7815 0.7811 0.7789 40774
weighted avg 0.7815 0.7811 0.7789 40774
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Model tree for dima806/galaxy_type_image_detection
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/galaxy_type_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")