README.md

metadata

library_name: autogluon
tags:
  - image-classification
  - autogluon
  - automl
  - sign-identification
datasets:
  - ecopus/sign_identification
metrics:
  - accuracy
  - f1
model-index:
  - name: sign-identification-autogluon
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          type: ecopus/sign_identification
          name: Sign Identification
        metrics:
          - type: accuracy
            value: 0.833
            name: Test Accuracy
          - type: f1
            value: 0.829
            name: Test F1 Score (Weighted)

Sign Identification with AutoGluon

This model was trained using AutoGluon's AutoML capabilities for sign identification. Performance not good enough because of the limited amount of data (only 30 images).

Model Description

• Framework: AutoGluon MultiModal
• Task: Multi-class image classification (N classes)
• Dataset: ecopus/sign_identification
• Architecture: ResNet18 (timm_image)
• Training: medium_quality preset

Performance

Metric	Validation	Test
Accuracy	0.833	0.571
F1 Score (Weighted)	0.829	0.571

Usage

Download and Load Model (Recommended — Native Directory)

from autogluon.multimodal import MultiModalPredictor
from huggingface_hub import hf_hub_download
import zipfile
import os

# Download the zipped predictor directory
zip_path = hf_hub_download(
    repo_id="yusenthebot/sign-identification-autogluon",
    filename="autogluon_sign_predictor_dir.zip"
)

# Extract
extract_dir = "predictor_dir"
os.makedirs(extract_dir, exist_ok=True)
with zipfile.ZipFile(zip_path, "r") as zf:
    zf.extractall(extract_dir)

# Load predictor
predictor = MultiModalPredictor.load(extract_dir)

# Predict (replace `your_dataframe` with a pandas DataFrame that matches training schema)
# predictions = predictor.predict(your_dataframe)