MITLL
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LADI-v2-classifier-small

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 ---
 library_name: transformers
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 ---
+license: mit
 library_name: transformers
+widget:
+- 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
+- example_title: Example classification of flooded scene
+pipeline_tag: image-classification
+tags:
+- LADI
+- Aerial Imagery
+- Disaster Response
+- Emergency Management
 ---
+# Model Card for MITLL/LADI-v2-classifier-small
+LADI-v2-classifier-small 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.
+📘 __NOTE__ 📘 This model is the main version of the small model which is trained on all splits of the LADI v2 dataset. It is intended for deployment and fine-tuning purposes. If you are interested in reproducing the results of our paper, see the 'reference' versions of the classifiers [MITLL/LADI-v2-classifier-small-reference](https://huggingface.co/MITLL/LADI-v2-classifier-small-reference) and [MITLL/LADI-v2-classifier-large-reference](https://huggingface.co/MITLL/LADI-v2-classifier-large-reference) models, which are trained only on the training split of the dataset.
 ## Model Details
 ### Model Description
+The model architecture is based on Google's bit-50 model and fine-tuned on the LADI v2 dataset, which contains 10,000 aerial images labeled by volunteers from the Civil Air Patrol. The images are labeled using multi-label classification for the following classes:
+- bridges_any
+- buildings_any
+- buildings_affected_or_greater
+- buildings_minor_or_greater
+- debris_any
+- flooding_any
+- flooding_structures
+- roads_any
+- roads_damage
+- trees_any
+- trees_damage
+- water_any
+- **Developed by:** Jeff Liu, Sam Scheele
+- **Funded by:** Department of the Air Force under Air Force Contract No. FA8702-15-D-0001
+- **License:** MIT
+- **Finetuned from model:** [google/bit-50](https://huggingface.co/google/bit-50)
 ## How to Get Started with the Model
+LADI-v2-classifier-small is trained to identify features of interest to disaster response managers from aerial images. Use the code below to get started with the model.
+The simplest way to perform inference is using the pipeline interface
+```python
+from transformers import pipeline
+image_url = "https://fema-cap-imagery.s3.amazonaws.com/Images/CAP_-_Flooding_Spring_2023/Source/IAWG_23-B-5061/A0005/D75_0793_DxO_PL6_P.jpg"
+pipe = pipeline(model="MITLL/LADI-v2-classifier-large")
+print(pipe(image_url))
+```
+```
+[{'label': 'flooding_any', 'score': 0.999765932559967},
+ {'label': 'flooding_structures', 'score': 0.9991484880447388},
+ {'label': 'buildings_any', 'score': 0.998734176158905},
+ {'label': 'water_any', 'score': 0.996557354927063},
+ {'label': 'buildings_affected_or_greater', 'score': 0.9952601790428162}]
+```
+For finer-grained control, see below:
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
+image_url = "https://fema-cap-imagery.s3.amazonaws.com/Images/CAP_-_Flooding_Spring_2023/Source/IAWG_23-B-5061/A0005/D75_0793_DxO_PL6_P.jpg"
+img_data = requests.get(image_url).content
+img = Image.open(BytesIO(img_data))
+processor = AutoImageProcessor.from_pretrained("MITLL/LADI-v2-classifier-large")
+model = AutoModelForImageClassification.from_pretrained("MITLL/LADI-v2-classifier-large")
+inputs = processor(img, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+predictions = torch.sigmoid(logits).detach().numpy()[0]
+labels = [(model.config.id2label[idx], predictions[idx]) for idx in range(len(predictions))]
+print(labels)
+```
+```
+[('bridges_any', 0.04825277253985405),
+ ('buildings_any', 0.998734176158905),
+ ('buildings_affected_or_greater', 0.9952601790428162),
+ ('buildings_minor_or_greater', 0.5874940752983093),
+ ('debris_any', 0.1582988053560257),
+ ('flooding_any', 0.999765932559967),
+ ('flooding_structures', 0.9991484880447388),
+ ('roads_any', 0.7687021493911743),
+ ('roads_damage', 0.9690849781036377),
+ ('trees_any', 0.7712554335594177),
+ ('trees_damage', 0.8490118384361267),
+ ('water_any', 0.996557354927063)]
+```
+## Citation
 **BibTeX:**
+```
+```
+Paper forthcoming - watch this space for details
+---
+DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
+This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.
+© 2024 Massachusetts Institute of Technology.
+The software/firmware is provided to you on an As-Is basis
+Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.