MITLL
/

LADI-v2-classifier-large

@@ -1,201 +1,128 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+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-large
+LADI-v2-classifier-large is based on [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) 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 large 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
+This 'reference' model is trained only on the training split, which contains 8,000 images from 2015-2022. It is provided for the purpose of reproducing the results from the paper. The 'deploy' model is trained on the training, validation, and test sets, and contains 10,000 images from 2015-2023. We recommend that anyone who wishes to use this model in production use the main versions of the models [MITLL/LADI-v2-classifier-small](https://huggingface.co/MITLL/LADI-v2-classifier-small) and [MITLL/LADI-v2-classifier-large](https://huggingface.co/MITLL/LADI-v2-classifier-large).
+- **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:** [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft)
 ## How to Get Started with the Model
+LADI-v2-classifier-large 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': 'water_any', 'score': 0.998600423336029},
+ {'label': 'buildings_any', 'score': 0.9936745166778564},
+ {'label': 'roads_any', 'score': 0.9805088043212891},
+ {'label': 'trees_any', 'score': 0.9748439192771912},
+ {'label': 'bridges_any', 'score': 0.4565260708332062}]
+```
+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', 0.4565260708332062),
+ ('buildings_any', 0.9936745166778564),
+ ('buildings_affected_or_greater', 0.39488333463668823),
+ ('buildings_minor_or_greater', 0.0001674541417742148),
+ ('debris_any', 0.00040665006963536143),
+ ('flooding_any', 0.013466577045619488),
+ ('flooding_structures', 0.04397346451878548),
+ ('roads_any', 0.9805088043212891),
+ ('roads_damage', 0.006721241865307093),
+ ('trees_any', 0.9748439192771912),
+ ('trees_damage', 0.06994925439357758),
+ ('water_any', 0.998600423336029)]
+```
+## 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.