ResNet-18 Noisy Image Classification
| Property | Value |
|---|---|
| Model Type | Binary Image Classifier |
| Architecture | ResNet-18 |
| Input Size | 224x224 RGB |
| Performance | 95.2% Accuracy |
| Model URL | HuggingFace |
What is resnet18-noisy-image-classification?
This is a specialized ResNet-18 model fine-tuned for the binary classification of images as either clean (high-quality) or noisy (distorted). The model has been optimized through dynamic quantization, making it efficient for real-world applications while maintaining high accuracy of 95.2% on validation data.
Implementation Details
The model utilizes the ResNet-18 architecture with a modified final fully connected layer to output two classes. It employs dynamic quantization to reduce model size and improve inference speed while maintaining accuracy. The model processes 224x224 RGB images and normalizes them using mean=[0.5, 0.5, 0.5] and std=[0.5, 0.5, 0.5].
- Custom dataset training with clean and noisy images
- Dynamic quantization applied to FC layers
- 8-bit integer precision for efficiency
- PyTorch implementation with easy-to-use inference pipeline
Core Capabilities
- Binary classification of clean vs. noisy images
- Fast inference speed due to quantization
- High accuracy (95.2%) and precision (94.5%)
- Robust performance metrics with 94.1% F1-Score
Frequently Asked Questions
Q: What makes this model unique?
This model combines the robust ResNet-18 architecture with dynamic quantization, offering an optimal balance between accuracy and efficiency in distinguishing clean from noisy images. Its high performance metrics and optimized inference speed make it particularly suitable for real-world applications.
Q: What are the recommended use cases?
The model is ideal for image quality assessment systems, automatic image filtering pipelines, and pre-processing steps in computer vision applications where image quality needs to be verified. However, users should be aware of potential limitations with images that have unusual lighting or artifacts not present in the training data.
