Push Keras model using huggingface_hub.

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 Baseline_Unet_Ash_Image_50_epochs.keras filter=lfs diff=lfs merge=lfs -text
+variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-
-widget:
-- src: https://huggingface.co/MIDSCapstoneTeam/ContrailSentinel/blob/main/contrail_shot.png
-  example_title: contrail1
-#- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
-#  example_title: Airport
-
+library_name: keras
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This model serves as a resource for researchers and data scientists seeking to identify contrails in satellite images. 
-Contrails (streams of vapor from airplane exhaust) can spread to many kilometers wide and trap heat in the atmosphere, which contributes to global warming. 
-Rerouting planes to avoid the atmospheric conditions that lead to contrail formation is an effective strategy, 
-and other researchers are already working on models that predict contrails. However, there is a need to validate those prediction models based on a "ground truth"
-indicating when and where contrails did/did not actually form. This model provides that ground truth, and should be used to help improve other models 
-that predict contrail formation.
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** UC Berkeley Master of Information and Data Science (MIDS) Capstone Team: Pedro Melendez, Prakash Krishnan, Rebecca Nissan, Sitao Chen, Ziling Huang
-- **Model type:** [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 Data 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 Data 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]
+## Model description
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+More information needed
 
-[More Information Needed]
+## Intended uses & limitations
 
-## More Information [optional]
+More information needed
 
-[More Information Needed]
+## Training and evaluation data
 
-## Model Card Authors [optional]
+More information needed
 
-[More Information Needed]
+## Training procedure
 
-## Model Card Contact
+### Training hyperparameters
 
-[More Information Needed]
+The following hyperparameters were used during training:
 
+| Hyperparameters | Value |
+| :-- | :-- |
+| name | RMSprop |
+| weight_decay | None |
+| clipnorm | None |
+| global_clipnorm | None |
+| clipvalue | None |
+| use_ema | False |
+| ema_momentum | 0.99 |
+| ema_overwrite_frequency | 100 |
+| jit_compile | True |
+| is_legacy_optimizer | False |
+| learning_rate | 0.0010000000474974513 |
+| rho | 0.9 |
+| momentum | 0.0 |
+| epsilon | 1e-07 |
+| centered | False |
+| training_precision | float32 |
 

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keras_metadata.pb

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variables/variables.data-00000-of-00001

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