Image Classification
Transformers
Safetensors
swinv2
LADI
Aerial Imagery
Disaster Response
Emergency Management
Instructions to use MITLL/LADI-v2-classifier-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MITLL/LADI-v2-classifier-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MITLL/LADI-v2-classifier-large") 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("MITLL/LADI-v2-classifier-large") model = AutoModelForImageClassification.from_pretrained("MITLL/LADI-v2-classifier-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ab40b1793aec10ed5e99ce85ac99ca6dca78ed01d908faa641d8b0609d25de2
- Size of remote file:
- 781 MB
- SHA256:
- b87ae57b12ea6c8a4ce9edce92b0ff0b055a26512a018cf264c7c56e8c109595
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.