ResNet-18
| Property | Value |
|---|---|
| Parameter Count | 11.7M parameters |
| License | Apache 2.0 |
| Framework Support | PyTorch, TensorFlow |
| Paper | Deep Residual Learning for Image Recognition |
| Dataset | ImageNet-1k |
What is ResNet-18?
ResNet-18 is a deep convolutional neural network architecture developed by Microsoft that revolutionized computer vision with its innovative residual learning framework. As part of the ResNet family, it features 18 layers and was trained on the ImageNet-1k dataset, making it highly effective for image classification tasks.
Implementation Details
The model implements residual connections that allow for better gradient flow during training, effectively solving the vanishing gradient problem in deep networks. It uses a F32 tensor type and provides both PyTorch and TensorFlow implementations for flexibility in deployment.
- Utilizes skip connections to enable deeper network training
- Trained on ImageNet-1k with 1000 classification categories
- Supports modern deep learning frameworks
- Optimized for production deployment with Inference Endpoints
Core Capabilities
- High-accuracy image classification across 1000 categories
- Efficient feature extraction for transfer learning
- Production-ready inference with multiple framework support
- Lightweight architecture with only 11.7M parameters
Frequently Asked Questions
Q: What makes this model unique?
ResNet-18's key innovation lies in its residual learning framework, which allows for training of much deeper networks than previously possible. Despite being lightweight with 11.7M parameters, it maintains strong performance on image classification tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks, transfer learning applications, and scenarios where a balance between model size and performance is crucial. It's ideal for production environments requiring reliable image analysis capabilities.
