---
license: mit
tags:
  - automl
  - autogluon
  - image-classification
  - neural-network
  - computer-vision
  - education
library_name: autogluon
datasets:
  - ecopus/sign_identification
model-index:
  - name: HW2 Neural AutoML — AutoGluon MultiModalPredictor (Signs)
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: ecopus/sign_identification
          type: ecopus/sign_identification
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4286
          - name: Macro-F1
            type: f1
            value: 0.3
---

# HW2 Neural AutoML — AutoGluon MultiModalPredictor (Signs)

## Model Overview
This model is a supervised image classification on a classmate’s dataset (`ecopus/sign_identification`) using **AutoGluon Multimodal**. 
It builds a compact model under a small compute budget and report results with a clear, reproducible pipeline.


## Summary

- **Backbone:** `resnet18` (via timm)
- **Input resolution:** 224×224 (images resized in Colab)
- **Train/Val/Test:** ~64% / 16% / 20% split (stratified)
- **Epochs:** 3 (short budget, early-stop not overridden)
- **Batch size:** 8
- **Metric (val):** Accuracy + Macro-F1
- **Result (test):** Accuracy = **0.4286**, Macro-F1 = **0.3**

## Dataset

- **Source:** `ecopus/sign_identification`
- **Task:** Multiclass sign recognition
- **Classes:** Stop, Yield, SpeedLimit, NoEntry, Crosswalk
- **Preprocessing:** 
  - `datasets` → decode to `PIL`
  - Resize to 224×224, RGB
  - Labels normalized to integers/strings for AutoGluon

## Training & AutoML Setup

**Library:** `autogluon.multimodal.MultiModalPredictor`  
**Problem type:** `multiclass`  
**Eval metric:** `accuracy` (Macro-F1 also reported)  

## AI Tool Disclosure
This notebook used ChatGPT for scaffolding code and documentation.
All dataset selection, training, evaluation, and uploads were performed by the student.