---
license: mit
datasets:
- ecopus/sign_identification
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is an image classifier that identifies images of stop signs. It is trained with Autogluon multimodal on the ecopus/sign_identification dataset.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model is an image classifier that identifies images of stop signs. It is trained with Autogluon multimodal on the ecopus/sign_identification dataset.

- **Developed by:** Sam Der
- **Model type:** AutoML (AutoGluon MultiModalPredictor with ResNet18 backbone)
- **License:** MIT

## 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. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is intended to be used to distinguish stop signs from other street signs.

## 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. -->

- dataset: ecopus/sign_identification
- splits:
  - original: 30 original images
  - augmented: 385 synthetic images

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- library: AutoGluon MultiModal
- presets: "medium_quality"
- backbone: timm_image → resnet18

#### Training Hyperparameters

- presets="medium_quality"
- hyperparameters={
    "model.names": ["timm_image"],
    "model.timm_image.checkpoint_name": "resnet18",
  }

## 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. -->

ecopus/sign_identification

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

- accuracy: fraction of correctly predicted labels
- F1 (weighted): harmonic mean of precision and recall, weighted by class support

### Results

accuracy: 1.0000 | weighted F1: 1.0000