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--- |
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license: mit |
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language: |
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- en |
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pretty_name: Lanternfly Image Classifier Training Dataset |
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datasets: |
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- rlogh/lanternfly-data |
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- rlogh/lanternfly_swatter_training |
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- rlogh/negativesirl |
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- uoft-cs/cifar100 |
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- AI-Lab-Makerere/beans |
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- Francesco/insects-mytwu |
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--- |
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# Dataset Card for {{ pretty_name | default("Dataset Name", true) }} |
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This dataset is the training dataset for 24-679 Project 1: Lanternfly Tracker |
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It is composed of 360 original lanternfly photos, 150 original photos with no lanternflies, and 800 original photos |
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from nature, urban, and other insect datasets listed below. |
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These were augmented 50X to 65.1k augmented images. |
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## Dataset Details |
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### Dataset Description |
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- **Curated by:** Carnegie Mellon University: 24-679 |
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- **Shared by [optional]:** Devin DeCosmo |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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### Dataset Sources [optional] |
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Original Lanternfly Datasets |
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rlogh/lanternfly-data: Original Lanternfly Dataset, 229 unmarked photos |
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rlogh/lanternfly_swatter_training: Dataset with geolocal data: 165 photos |
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Original Negative Datasets: |
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rlogh/negativesirl: Negatives dataset, images of outdoor environements and people with no lanternflies. 107 photos |
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Total: 501 original images |
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Imported Datasets |
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uoft-cs/cifar100: General image classifier, no insect class |
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AI-Lab-Makerere/beans: Foliage with no insects |
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Francesco/insects-mytwu: Insect Images |
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Total: 800 additional images imported |
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## Uses |
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These images were used to train the EfficientNetB1 model, ddecosmo/lanternfly_classifier, on how to classify images |
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as containing or not containing lanternflies. |
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### Direct Use |
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The direct use is identifying photographs containing lanterflies so this could be used for tracking purposes. |
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### Out-of-Scope Use |
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In future, this model could be adapted to identify other types of insect within this dataset. |
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## Dataset Structure |
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This dataset consists of two splits |
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An original split with 1.3k photos |
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An artificial split with 65.1k photos |
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The tasks fall into 3 categories based on the building pictured |
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1. Lanternflies, all original photos |
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2. Other Insect, all 3rd party datasets |
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3. No insect, original photos and 3rd party datasets |
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## Dataset Creation |
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### Source Data |
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This data is sourced by the creators, Devin and Rumi for all original photos |
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Additional datasets can be found here, |
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uoft-cs/cifar100 |
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AI-Lab-Makerere/beans |
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Francesco/insects-mytwu |
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#### Data Collection and Processing |
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Original datasets were collected using the mobile phones of the authors. |
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Additional datasets were recommended by Gemini AI and then validated as fitting the purpose, type, and scope of this process. |
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uoft-cs/cifar100: This is a general image identifier with no insect class. Used for no insect for generalizability |
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AI-Lab-Makerere/beans: This dataset is focused on vegetation with and without disease, this is used to train the model to recognize |
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vegetation without insects/lanterflies. |
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Francesco/insects-mytwu: This is an object detection dataset used for identifying insects as subjects, not including lanterflies. |
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We are using it train a seperate non-lanternfly insect class. |
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#### Who are the source data producers? |
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Original data was produced by the authors. |
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Additional datasets were produced by, |
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uoft-cs/cifar100: Created by University of Toronto Computer Science |
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AI-Lab-Makerere/beans: Created by AI Lab Makere |
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Francesco/insects-mytwu: Created by Fanscesco Sovrano |
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## Bias, Risks, and Limitations |
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The main risk of this dataset is the lanternfly split. It contains only images of singular lanternflies on the ground. |
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Normally on concrete or asphalt. This severly limits the scope of the environments these creatures appear in. |
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Incorporating blob detection or YOLO into future models could mitigate this by focusing on the subject. |
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### Recommendations |
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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. |
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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. |
