Update README.md

README.md

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 ---
+'[object Object]': null
 license: mit
 datasets:
 - maryzhang/hw1-24679-image-dataset
 language:
 - en
-metrics:
-- accuracy
-base_model:
-- timm/mobilenetv3_small_100.lamb_in1k
-pipeline_tag: image-classification
----
+---
+
+# Model Card for {{ model_id | default("Model ID", true) }}
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is a fine tuned version of the TimmAutoModel for classification. This was fine tuned on the maryzhang/hw1-24679-image-dataset which is a dataset of Western and Asian dishes used for binary classification. 
+
+## Model Details
+
+### Model Description
+
+This model uses the TimmAutoModel with accuracy as the main parameter and multi class accuracy and cross entropy as the main hyperparameters. 
+
+- **Developed by:** Devin DeCosmo
+- **Model type:** Image Classifier
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** TimmAutoModel
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** {{ repo | default("[More Information Needed]", true)}}
+
+
+## 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. -->
+
+This could be used for general image classification tasks, especially those for culinary uses. 
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+The direct use would be to classify food as either Western or Asian based on an image. 
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+If the dataset was expanded, this could be used to classify other types of food among numerous other classes. 
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+This is trained off a small dataset of 30 original photos and 300 augmented photos. This could suggest overfitting of the model and additional information is required to make it more robust.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+The small dataset size means this model is not highly generalizable. 
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+## 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. -->
+
+maryzhang/hw1-24679-image-dataset
+
+This is the training dataset used. 
+It consists of 30 original images used for validation along with 300 synthetic pieces of data from training. 
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+This model was trained with an AutoML process with accuracy as the main metrics. The modelw as trained over 20 epochs with a batch size of 32 images. 
+
+
+#### Training Hyperparameters
+
+- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+
+## 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. -->
+maryzhang/hw1-24679-image-dataset
+The testing data was the 'original' split, the 30 original images in this set. 
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+This dataset is evaluating whether the food is Western, "1", or Asian, "0".
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+The testing metric used was accuracy to ensure the highest accuracy of the model possible. 
+
+
+### Results
+
+After training with the initial dataset, this model reached an accuracy of 95% in validation. 
+
+#### Summary
+
+This model reached a high accuracy with our current model, but this perfomance can not be confirmed to continue as the dataset was very small. 
+