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README.md
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# Model Card for Model ID
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# Model Details
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## Model Description
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<!-- Provide a longer summary of what this model is. -->
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This model is a small resnet18 trained on cifar100.
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- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** en
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- **License:** mit
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- **Finetuned from model [optional]:** [More Information Needed]
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## Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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# Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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## Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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## Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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## Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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# Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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## Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Training Data
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<!-- 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. -->
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## Training Procedure [optional]
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[More Information Needed]
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### Speeds, Sizes, Times
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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# Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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## Testing Data, Factors & Metrics
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### Testing Data
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<!-- This should link to a Data Card if possible. -->
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### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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## Results
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### Summary
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# Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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# Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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# Technical Specifications [optional]
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## Model Architecture and Objective
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[More Information Needed]
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## Compute Infrastructure
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[More Information Needed]
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### Hardware
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[More Information Needed]
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### Software
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[More Information Needed]
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# Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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# Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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# More Information [optional]
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[More Information Needed]
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# Model Card Authors [optional]
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[More Information Needed]
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# Model Card Contact
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[More Information Needed]
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# Model Card for Model ID
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This model is a small resnet18 trained on cifar100. It achieves the following results on the evaluation set: Accuracy: 0.7843.
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- **Developed by:** Eduardo Dadalto
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- **License:** MIT
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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import timm
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model = timm.create_model("{model_name}", pretrained=True)
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```
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## Training Data
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<!-- 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. -->
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Training data is cifar100.
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## Training Hyperparameters
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## Evaluation
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### Testing Data
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<!-- This should link to a Data Card if possible. -->
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Testing data is cifar100.
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### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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Accuracy
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## Results
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Accuracy is 0.7843.
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