Model Card for {{ model_id | default("Model ID", true) }}
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]
- Repository: {{ repo | default("[More Information Needed]", true)}}
Uses
This could be used for general image classification tasks, especially those for culinary uses.
Direct Use
The direct use would be to classify food as either Western or Asian based on an image.
Out-of-Scope Use
If the dataset was expanded, this could be used to classify other types of food among numerous other classes.
Bias, Risks, and 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
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
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 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
This model used an Adam optimizer, mulit-class accuracy, and cross entropy loss.
Evaluation
Testing Data, Factors & Metrics
Testing Data
maryzhang/hw1-24679-image-dataset
The testing data was the 'original' split, the 30 original images in this set.
Factors
This dataset is evaluating whether the food is Western, "1", or Asian, "0".
Metrics
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.
