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ccm/
2025-24679-image-autogluon-predictor

Name: 2025-24679-image-autogluon-predictor
Availability: InStock

autogluon

images

image-classification

ccm/2025-24679-image-dataset

mit

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Model Card for Image AutoML Predictor

Binary/multiclass image classifier trained with AutoGluon MultiModal on the augmented split of ccm/2025-24679-image-dataset to predict survey-derived image labels. Metrics are reported on a held-out test portion of the augmented split and evaluated via external validation on the original split. Artifacts include (1) a zipped native AutoGluon predictor directory (recommended) and (2) a cloudpickled predictor (for convenience).

Model Details

Model Description

Developed by: Fall 2025 24-679 (CMU) — instructor: Christopher McComb
Shared by: Christopher McComb
Model type: AutoML (AutoGluon MultiModalPredictor with ResNet18 backbone)
Task: Image classification
Target column: label
License: MIT
Framework: autogluon.multimodal
Repo artifacts:
- autogluon_image_predictor_dir.zip (zipped native predictor directory)
- autogluon_image_predictor.pkl (cloudpickled predictor)

Uses

Direct Use

Classroom demos of AutoML for image classification
Baseline experiments for augmentation vs. generalization
Comparing augmented vs original split performance

Out-of-Scope Use

Production deployment with sensitive/real-world decision stakes
Generalization beyond course context or survey-specific images

Bias, Risks, and Limitations

Synthetic data inflation: Augmented data may artificially boost in-split accuracy.
Limited representativeness: Original dataset is small, student-generated, not diverse.
Label noise: Survey/image associations may be noisy or inconsistent.

Recommendations

Always report both augmented-test and original-validation metrics.
Emphasize didactic use cases (education, experimentation).
Use consistent random seeds and splits for reproducibility.

How to Get Started with the Model

python

import pathlib, shutil, zipfile
import huggingface_hub as hf
from autogluon.multimodal import MultiModalPredictor

REPO = "ccm/2025-24679-image-autogluon-predictor"
ZIPNAME = "autogluon_image_predictor_dir.zip"

dest = pathlib.Path("hf_download")
dest.mkdir(exist_ok=True)

# Download predictor zip
zip_path = hf.hf_hub_download(
    repo_id=REPO,
    filename=ZIPNAME,
    repo_type="model",
    local_dir=str(dest),
    local_dir_use_symlinks=False,
)

# Extract
extract_dir = dest / "predictor_dir"
if extract_dir.exists():
    shutil.rmtree(extract_dir)
extract_dir.mkdir(parents=True, exist_ok=True)

with zipfile.ZipFile(zip_path, "r") as zf:
    zf.extractall(str(extract_dir))

# Load predictor
predictor = MultiModalPredictor.load(str(extract_dir))

# Example inference
preds = predictor.predict(test_df[["image"]])

Training Details

Training Data

Dataset: ccm/2025-24679-image-dataset
Splits:
- Augmented: 80/20 train/test with stratification (random_state=42)
- Validation: 20% of train used as val split
- External validation: Entire original split (unused in training)

Training Procedure

Library: AutoGluon MultiModal
Presets: "medium_quality"
Backbone: timm_image → resnet18
Training time limit: default (few minutes)
Eval metric: Accuracy

Hyperparameters

model.names: timm_image
checkpoint: resnet18
presets: medium_quality
random_state: 42

Evaluation

Testing Data

Augmented test: Held-out 20% of augmented split
External validation: Entire original split

Metrics

Accuracy: % correct predictions
Weighted F1: Harmonic mean of precision/recall, weighted by support

Results (example — replace with actuals)

Augmented test: Accuracy = 0.7429, Weighted F1 = 0.7392
Original validation: Accuracy = 0.8621, Weighted F1 = 0.8620

Environmental Impact

Hardware: Single GPU (short run)
Training wall-time: < 10 minutes
Estimated emissions: negligible
Cloud provider: N/A (depends on student setup)

See ML CO₂ calculator for custom estimates.

Model Card Contact

Christopher McComb — ccm@cmu.edu