Add README.md with model card

README.md

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+---
+tags:
+- autogluon
+- multimodal
+- image-classification
+- binary-classification
+- ensemble-learning
+- education
+- homework
+datasets:
+- ecopus/sign_identification
+license: mit
+---
+
+# HW1 Sign Identification with AutoGluon
+
+## Model Description
+This repository contains an **AutoML image classification model trained with AutoGluon Multimodal**  
+to identify two categories of sign images.  
+The model was trained as part of **Homework 1** in CMU 24-679 (Designing and Deploying AI/ML).  
+
+- **Framework**: [AutoGluon Multimodal](https://auto.gluon.ai/stable/tutorials/multimodal/index.html)  
+- **Backbone**: TimmAutoModelForImagePrediction (~194M parameters)  
+- **Task**: Binary image classification (`label`)  
+- **Classes**: `0`, `1`  
+
+---
+
+## Training Setup
+- **Dataset**: [ecopus/sign_identification](https://huggingface.co/datasets/ecopus/sign_identification)  
+  - `augmented` split (385 samples) → used for training/validation (80/20).  
+  - `original` split (35 samples) → reserved for final testing.  
+- **Time budget**: 300 seconds (≈7 minutes).  
+- **Hardware**: Colab GPU (CUDA 12.6, mixed precision).  
+- **Presets**: `best_quality` (ensembling + hyperparameter tuning).  
+
+---
+
+## Results
+- **Validation ROC-AUC**: 0.998  
+- **Test Accuracy**: 97.1%  
+- **Weighted F1**: 97.1%  
+
+### Classification Report (Test Set)
+
+
+          precision    recall  f1-score   support
+       0       0.95      1.00      0.97        19
+       1       1.00      0.94      0.97        16
+accuracy                           0.97        35
+
+
+macro avg 0.97 0.97 0.97 35
+weighted avg 0.97 0.97 0.97 35
+
+---
+
+## How to Use
+
+### Install requirements
+```bash
+pip install autogluon.multimodal huggingface_hub cloudpickle
+
+import cloudpickle
+from huggingface_hub import hf_hub_download
+
+pkl_path = hf_hub_download(
+    repo_id="cassieli226/sign-identification-automl",
+    filename="autogluon_predictor.pkl",
+    repo_type="model"
+)
+
+with open(pkl_path, "rb") as f:
+    predictor = cloudpickle.load(f)
+
+# Predict on new data (expects DataFrame with 'image' column containing file paths)
+import pandas as pd
+X_test = pd.DataFrame({"image": ["path/to/your/image.png"]})
+print(predictor.predict(X_test))
+
+
+import pathlib, shutil, zipfile
+from huggingface_hub import hf_hub_download
+import autogluon.multimodal as ag
+import pandas as pd
+
+zip_path = hf_hub_download(
+    repo_id="cassieli226/sign-identification-automl",
+    filename="autogluon_predictor_dir.zip",
+    repo_type="model"
+)
+
+extract_dir = pathlib.Path("predictor_dir")
+if extract_dir.exists():
+    shutil.rmtree(extract_dir)
+with zipfile.ZipFile(zip_path, "r") as zf:
+    zf.extractall(str(extract_dir))
+
+predictor = ag.MultiModalPredictor.load(str(extract_dir))
+print(predictor.predict(pd.DataFrame({"image": ["path/to/your/image.png"]})))
+
+
+#Intended Use
+ - Coursework demonstration of AutoML for neural networks on images.
+ - Educational example for using augmented vs. original splits for training and evaluation.
+#Limitations
+ - Trained on a small student-collected dataset (≈420 images).
+ - Accuracy may not generalize to unseen real-world data.
+ - Model assumes binary labels only (0, 1).
+#Ethical Notes
+ - Dataset is non-sensitive, contains no personal information.
+ - Augmentation was applied responsibly to avoid unrealistic samples.
+# References
+ - Dataset: ecopus/sign_identification
+ - Framework: AutoGluon
+ - OpenAI’s ChatGPT (2025) was used for code generation, structuring, and debugging.