Image Classification
Transformers
Safetensors
swinv2
LADI
Aerial Imagery
Disaster Response
Emergency Management
Instructions to use MITLL/LADI-v2-classifier-large-reference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MITLL/LADI-v2-classifier-large-reference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MITLL/LADI-v2-classifier-large-reference") 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-reference") model = AutoModelForImageClassification.from_pretrained("MITLL/LADI-v2-classifier-large-reference") - Notebooks
- Google Colab
- Kaggle
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## How to Get Started with the Model
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LADI-v2-classifier-
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The simplest way to perform inference is using the pipeline interface
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## How to Get Started with the Model
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LADI-v2-classifier-large-reference is trained to identify features of interest to disaster response managers from aerial images. Use the code below to get started with the model.
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The simplest way to perform inference is using the pipeline interface
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