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
add citation
Browse files
README.md
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**BibTeX:**
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Paper forthcoming - watch this space for details
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---
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DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
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**BibTeX:**
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```
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@misc{ladi_v2,
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title={LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery},
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author={Samuel Scheele and Katherine Picchione and Jeffrey Liu},
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year={2024},
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eprint={2406.02780},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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---
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DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
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