2025-24679-image-autogluon-predictor

Model Card: Book Cover Genre Classifier (Fiction/Non-Fiction)

Model Details

• Model Type: Fine-tuned Vision Transformer (or specify other architecture, e.g., ResNet) for binary image classification.
• Developed by: [Zachary Zdobinski]
• Model Date: September 19, 2025

Intended Use

This model is designed to classify images of book covers into one of two categories: fiction or non-fiction. Its primary intended use is for automated library systems, digital bookstore cataloging, or personal collection management tools where the genre needs to be inferred directly from the cover art.

Training Data

The model was trained on a custom dataset of book cover images. Each image in the dataset was manually labeled as either fiction or non-fiction. The dataset was split into training, validation, and test sets to ensure robust evaluation.

Training Procedure

The model was trained for 13 epochs. The training process was optimized using the AdamW optimizer with a learning rate of [Specify Learning Rate].

• Training Time: The total training time was approximately 160 seconds on the hardware used.
• Framework: PyTorch

Evaluation

The model's performance was evaluated on a held-out validation set. It achieved perfect scores across key classification metrics, indicating a very high level of performance on data similar to its training set.

Limitations and Bias

• Visual Cues Only: The model relies exclusively on the visual information present on the book cover. It cannot read text (such as title or author) and has no knowledge of the book's content. Covers that are stylistically ambiguous or minimalist may be misclassified.
• Dataset Scope: The model's performance is highly dependent on the diversity and representativeness of the training data. It may perform poorly on book covers from genres, time periods, or artistic styles not well-represented in the training set.
• Risk of Overfitting: A perfect validation score may suggest that the model has learned the specific patterns of the training/validation data very well, but it might not generalize perfectly to completely new, unseen book covers from the real world.
• Cultural and Temporal Bias: Book cover design trends change over time and vary by culture. The model may exhibit biases based on the publication era or geographic origin of the books in its training set.

How to Use

To use this model, load the trained PyTorch checkpoint and an appropriate image transformation pipeline.