Image Classification
Transformers
Safetensors
bit
LADI
Aerial Imagery
Disaster Response
Emergency Management
Instructions to use MITLL/LADI-v2-classifier-small-reference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MITLL/LADI-v2-classifier-small-reference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MITLL/LADI-v2-classifier-small-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-small-reference") model = AutoModelForImageClassification.from_pretrained("MITLL/LADI-v2-classifier-small-reference") - Notebooks
- Google Colab
- Kaggle
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- src: https://fema-cap-imagery.s3.amazonaws.com/Images/CAP_-_Flooding_Spring_2023/Source/IAWG_23-B-5061/A0005/D75_0793_DxO_PL6_P.jpg
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- example_title: Example classification of flooded scene
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pipeline_tag: image-classification
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---
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# Model Card for MITLL/LADI-v2-classifier-small-reference
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LADI-v2-classifier-small-reference is based on [google/bit-50](https://huggingface.co/google/bit-50) and fine-tuned on the LADI v2_resized dataset. LADI-v2-classifier is trained to identify labels of interest to disaster response managers from aerial images.
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- src: https://fema-cap-imagery.s3.amazonaws.com/Images/CAP_-_Flooding_Spring_2023/Source/IAWG_23-B-5061/A0005/D75_0793_DxO_PL6_P.jpg
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- example_title: Example classification of flooded scene
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pipeline_tag: image-classification
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tags:
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- LADI
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- Aerial Imagery
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- Disaster Response
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- Emergency Management
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---
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# Model Card for MITLL/LADI-v2-classifier-small-reference
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LADI-v2-classifier-small-reference is based on [google/bit-50](https://huggingface.co/google/bit-50) and fine-tuned on the LADI v2_resized dataset. LADI-v2-classifier is trained to identify labels of interest to disaster response managers from aerial images.
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