Update README.md

README.md

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 ---
+'[object Object]': null
 license: mit
+datasets:
+- ddecosmo/lanternfly_training_dataset
 language:
 - en
-pretty_name: Lanternfly Image Classifier Training Dataset
-datasets:
-- rlogh/lanternfly-data
-- rlogh/lanternfly_swatter_training
-- rlogh/negativesirl
-- uoft-cs/cifar100
-- AI-Lab-Makerere/beans
-- Francesco/insects-mytwu
 ---
 
+# Model Card for {{ model_id | default("Model ID", true) }}
 
-# Dataset Card for {{ pretty_name | default("Dataset Name", true) }}
+<!-- Provide a quick summary of what the model is/does. -->
 
-This dataset is the training dataset for 24-679 Project 1: Lanternfly Tracker
-It is composed of 360 original lanternfly photos, 150 original photos with no lanternflies, and 800 original photos
-from nature, urban, and other insect datasets listed below. 
+This is an off the shelf KDE model from SciPy. It is Kernel Density Estimator, 
+in this case it is used to track the relative density of lanternfly sightings in Pittsburgh. 
 
-These were augmented 50X to 65.1k augmented images. 
+## Model Details
 
+### Model Description
 
-## Dataset Details
+This model is a KDE. This is an unsupervised model that 
+estimates the density of continuous values from discrete points. 
 
-### Dataset Description
+This model is from the SciPy library and stored to allow for rapid access. 
 
-- **Curated by:** Carnegie Mellon University: 24-679
-- **Shared by [optional]:** Devin DeCosmo
+- **Developed by:** Devin DeCosmo
+- **Model type:** Image Classifier
 - **Language(s) (NLP):** English
 - **License:** MIT
+- **Finetuned from model:** SciPy Gaussian KDE
 
-### Dataset Sources [optional]
+## Uses
 
-Original Lanternfly Datasets
-  rlogh/lanternfly-data: Original Lanternfly Dataset, 229 unmarked photos
-  rlogh/lanternfly_swatter_training: Dataset with geolocal data: 165 photos
-  
-Original Negative Datasets:
-  rlogh/negativesirl: Negatives dataset, images of outdoor environements and people with no lanternflies. 107 photos
+<!-- 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 to estimate the density of values in proportion to each other.
+From 0 - 1. In this case, it uses longitude and latitude as X,Y coordinates to perform this analysis.
 
-Total: 501 original images
+### Direct Use
 
-Imported Datasets
-uoft-cs/cifar100: General image classifier, no insect class
-AI-Lab-Makerere/beans: Foliage with no insects
-Francesco/insects-mytwu: Insect Images
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-Total: 800 additional images imported
+The direct use is classifying our lanternfly sighting samples from our geolocal dataset. 
+As the Gaussian KDE is a generalized unsupervised learning model, this could be used
+for other datsets with latitude/longitude coordinates. 
 
+### Out-of-Scope Use
 
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-## Uses
 
-These images were used to train the EfficientNetB1 model, ddecosmo/lanternfly_classifier, on how to classify images 
-as containing or not containing lanternflies. 
 
+## Bias, Risks, and Limitations
 
-### Direct Use
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-The direct use is identifying photographs containing lanterflies so this could be used for tracking purposes. 
 
-### Out-of-Scope Use
 
-In future, this model could be adapted to identify other types of insect within this dataset. 
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
 
-## Dataset Structure
 
-This dataset consists of two splits
-An original split with 1.3k photos
-An artificial split with 65.1k photos
 
-The tasks fall into 3 categories based on the building pictured
-1. Lanternflies, all original photos
-2. Other Insect, all 3rd party datasets
-3. No insect, original photos and 3rd party datasets
+## Training Details
 
-## Dataset Creation
+### Training Data
 
-### Source 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. -->
 
-This data is sourced by the creators, Devin and Rumi for all original photos
+rlogh/lanternfly_swatter_training
 
-Additional datasets can be found here,
-uoft-cs/cifar100
-AI-Lab-Makerere/beans
-Francesco/insects-mytwu
+### Training Procedure
 
-#### Data Collection and Processing
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-Original datasets were collected using the mobile phones of the authors. 
 
-Additional datasets were recommended by Gemini AI and then validated as fitting the purpose, type, and scope of this process.
-uoft-cs/cifar100: This is a general image identifier with no insect class. Used for no insect for generalizability
-AI-Lab-Makerere/beans: This dataset is focused on vegetation with and without disease, this is used to train the model to recognize 
-vegetation without insects/lanterflies.
-Francesco/insects-mytwu: This is an object detection dataset used for identifying insects as subjects, not including lanterflies. 
-We are using it train a seperate non-lanternfly insect class. 
+#### Training Hyperparameters
 
-#### Who are the source data producers?
 
-Original data was produced by the authors. 
 
-Additional datasets were produced by, 
-uoft-cs/cifar100: Created by University of Toronto Computer Science
-AI-Lab-Makerere/beans: Created by AI Lab Makere
-Francesco/insects-mytwu: Created by Fanscesco Sovrano
+## Evaluation
 
-## Bias, Risks, and Limitations
+<!-- This section describes the evaluation protocols and provides the results. -->
 
-The main risk of this dataset is the lanternfly split. It contains only images of singular lanternflies on the ground.
-Normally on concrete or asphalt. This severly limits the scope of the environments these creatures appear in.
-Incorporating blob detection or YOLO into future models could mitigate this by focusing on the subject. 
+### Testing Data, Factors & Metrics
 
-### Recommendations
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+
+
+### Results
+
+
+#### Summary
 
-This is a large dataset, and has been shown to accurately classify lanternflies, but there are many edge cases when it does not work correctly. 
-In order to take this into account, using new types of models with subject detection can make use of the many images while improving model accuracy.