Create README.md

README.md

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+---
+'[object Object]': null
+license: mit
+datasets:
+- ddecosmo/lanternfly_training_dataset
+language:
+- en
+base_model:
+- google/efficientnet-b1
+---
+
+# 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 an EfficientNetB1 model trained on lanternfly, other insects, and general photos
+for classification. 
+
+## Model Details
+
+### Model Description
+
+This model uses an EfficientNEtB1 with an Adam optimizer, mulit-class accuracy, and cross entropy loss.
+
+- **Developed by:** Devin DeCosmo
+- **Model type:** Image Classifier
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** EfficientNetB1
+
+## 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 model is used for classifying lanterfly photos vs other insects and non insect photos.
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+The direct use is classiying photos within the 3 classes provided. Lanternfly, other insect, and non insect classes. 
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+This could be expanded to additional insect classes to expand range tracking capabilities. 
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+This model is trained off a subset of lanternfly, insect, and non lanternfly images. 
+The dataset is a moderate size with a large number of augmented values. 
+It is accurate to 95% within testing and validation but there are edge cases
+not included in the dataset that cause errors. 
+
+This includes insects in locations not included in training data and outdoor scenes with different lighting. 
+The dataset should be expanded or the model should be changed to improve it. 
+
+### 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. -->
+
+ddecosmo/lanternfly_training_dataset
+
+This is the training dataset used. 
+
+### 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
+
+This model used an Adam optimizer, mulit-class accuracy, and cross entropy loss.
+
+
+## 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. -->
+ddecosmo/lanternfly_training_dataset
+The testing data was the 'original' split, the original and 3rd party 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 Lanternfly, "0", or Other Insect, "1", or Non Insect "2".
+
+#### 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.
+The large size of the dataset allowed for a large amount of training. 
+After training, it was found the training dataset had gaps, causing edge case failures
+that fell outside the bounds of the original dataset.