Instructions to use Eki734/meteorite-dinov2-b14-direct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eki734/meteorite-dinov2-b14-direct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Eki734/meteorite-dinov2-b14-direct", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("Eki734/meteorite-dinov2-b14-direct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Meteorite DINOv2-B Direct Model
Direct-call Hugging Face custom model for meteorite image classification.
from transformers import AutoModelForImageClassification
from PIL import Image
model = AutoModelForImageClassification.from_pretrained(
"Eki734/meteorite-dinov2-b14-direct",
trust_remote_code=True,
)
image = Image.open("example.jpg")
result = model.predict(image)
print(float(result["prob_meteorite"][0]))
This repository stores the trained MLP probe head. The frozen DINOv2-B with registers backbone (dinov2_vitb14_reg) is loaded from torch.hub at runtime.
Public score of the corresponding Top-90 submission: 0.79545.
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