watermark_detector
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | google/vit-base-patch16-224-in21k |
| Best Accuracy | 65.74% |
| Framework | PyTorch |
What is watermark_detector?
The watermark_detector is a specialized image classification model built on the Vision Transformer (ViT) architecture. It's designed to detect watermarks in images, leveraging the powerful google/vit-base-patch16-224-in21k as its foundation. With over 20,000 downloads, this model demonstrates significant practical utility in the field of image analysis.
Implementation Details
The model utilizes a fine-tuned Vision Transformer architecture with the following training parameters: batch size of 16, Adam optimizer with carefully tuned parameters (β1=0.9, β2=0.999, ε=1e-08), and a linear learning rate scheduler starting at 5e-05. The training process spans 3 epochs, showing consistent improvement in both training and validation metrics.
- Training Loss: Improved from 0.6492 to 0.5780
- Validation Loss: Decreased from 0.6375 to 0.6110
- Accuracy: Increased from 62.62% to 65.08%
Core Capabilities
- Watermark Detection in Images
- Binary Classification
- Patch-based Image Analysis
- Transformer-based Feature Extraction
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of Vision Transformers with specialized training for watermark detection, achieving a balanced accuracy of 65.74% through careful optimization and linear learning rate scheduling.
Q: What are the recommended use cases?
The model is particularly suited for automated watermark detection in digital images, content verification systems, and digital rights management applications.
