| | --- |
| | '[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 gaps found within this dataset, other insects and other lighting conditions, mean this model cannot be trusted in |
| | all novel environment. Expanding this dataset or altering this model to include technique like |
| | blob identification would mitigate this issue. |
| | |
| | |
| | ## 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. |
