AutoGluon Sign Identification Predictor
This repository contains a trained MultiModalPredictor from the AutoGluon library, which was trained to identify signs from images. Which can also be found in the files and versions section under AutoML_for_Neural_Networks
Dataset
The model was trained on the ecopus/sign_identification dataset. The augmented split was used for training and validation, while the original split was used for the final evaluation of the model's performance.
Evaluation Results
The final performance of the best model on the original dataset is as follows:
- Accuracy:
1.0000
- Weighted F1:
1.0000
Files in this Repository
autogluon_image_predictor.pkl: The trained MultiModalPredictor pickled using cloudpickle.autogluon_image_predictor_dir.zip: The zipped native AutoGluon predictor directory for portability.
Potential Errors
The augmented split in the ecopus/sign_identification dataset is specifically designed to be an artificially expanded version of the original split.
The images in the augmented set are simple transformations—like rotations, flips, or slight color changes—of the images in the original set.
The code then trains the model on a portion of the augmented data (df_aug_train) and evaluates it on the original data (df_orig).
Because the model was trained on data that is derived directly from the evaluation data, it's not actually seeing truly "new" information during the final test.
Which could be leading to data leakage and overfitting
