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---
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.
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