Image Feature Extraction
Transformers
Safetensors
aloe_siglip2_vision
image-classification
aloe
b-cos
interpretability
computer-vision
vision-transformer
feature-extraction
cvpr-2026
custom_code
Instructions to use rmaser/aloe-siglip2-so400m-in1k-lp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmaser/aloe-siglip2-so400m-in1k-lp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="rmaser/aloe-siglip2-so400m-in1k-lp", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("rmaser/aloe-siglip2-so400m-in1k-lp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 9036937bffb67a68ab28343d1a82745fe3eea7e54c3a72e0ce791b0cc4037123
- Size of remote file:
- 1.21 MB
- SHA256:
- 5d066ce57a0fddbe518470d242c77976130e12b5bca095f8ccd7ba418556972f
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