ResNet-152
| Property | Value |
|---|---|
| Parameter Count | 60.3M parameters |
| License | Apache 2.0 |
| Framework | PyTorch |
| Dataset | ImageNet-1k |
| Paper | Deep Residual Learning for Image Recognition |
What is ResNet-152?
ResNet-152 is a deep convolutional neural network model that implements the revolutionary residual learning framework. Developed by Microsoft, this model represents version 1.5 of the original ResNet architecture, featuring 152 layers and trained on the ImageNet-1k dataset for image classification tasks.
Implementation Details
This implementation differs from the original ResNet v1 in its bottleneck blocks. In v1.5, the stride=2 operation is performed in the 3x3 convolution rather than the first 1x1 convolution, resulting in slightly better accuracy (~0.5% top1) with a minor performance trade-off (~5% imgs/sec).
- Architecture based on residual learning principles
- Trained on ImageNet-1k at 224x224 resolution
- Uses skip connections to enable deeper network training
- Implements F32 tensor type for computations
Core Capabilities
- High-accuracy image classification across 1,000 ImageNet classes
- Robust feature extraction for transfer learning
- Efficient training through residual connections
- Compatible with modern deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
ResNet-152 stands out for its deep architecture with 152 layers, made possible through residual learning. The v1.5 variant offers improved accuracy over the original version while maintaining practical efficiency.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, particularly when dealing with complex visual recognition challenges. It can be used both as a standalone classifier and as a backbone for transfer learning in various computer vision applications.
