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---
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](https://huggingface.co/datasets/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)
```python
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)
|