edadaltocg
/

resnet18_cifar100

@@ -24,191 +24,42 @@ model-index:
 # 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 model is a small resnet18 trained on cifar100.
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** mit
-- **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 [optional]
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-### Preprocessing
-[More Information Needed]
-### Speeds, Sizes, Times
-<!-- 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]
-<!-- 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]

 # Model Card for Model ID
+This model is a small resnet18 trained on cifar100. It achieves the following results on the evaluation set: Accuracy: 0.7843.
+- **Developed by:** Eduardo Dadalto
+- **License:** MIT
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+import timm
+model = timm.create_model("{model_name}", pretrained=True)
+```
 ## 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. -->
+Training data is cifar100.
+## Training Hyperparameters
+## Evaluation
 ### Testing Data
 <!-- This should link to a Data Card if possible. -->
+Testing data is cifar100.
 ### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Accuracy
 ## Results
+Accuracy is 0.7843.