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-432-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-432-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-432-in1k-lp", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("rmaser/aloe-siglip2-so400m-432-in1k-lp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- a715cd1db3e6535ab5de2e00cbd75deb737f1043e1f2262a361a2012b5e8dfff
- Size of remote file:
- 124 kB
- SHA256:
- c78bc9caee6b037c53a7e4053e500b11df6564a68b7cd91bf6fcd155d689812e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.