ResNet-34
| Property | Value |
|---|---|
| Model Author | Microsoft |
| Paper | Deep Residual Learning for Image Recognition (He et al., 2016) |
| Architecture | ResNet v1.5 |
| Input Resolution | 224x224 |
| Training Data | ImageNet-1k |
What is ResNet-34?
ResNet-34 is a powerful convolutional neural network that implements the revolutionary concept of residual learning and skip connections. This version (v1.5) is an improved variant of the original ResNet architecture, featuring modified stride placements in bottleneck blocks that enhance accuracy while maintaining computational efficiency.
Implementation Details
ResNet-34 v1.5 differs from v1 in its downsampling approach: while the original version applies stride=2 in the first 1x1 convolution of bottleneck blocks, v1.5 applies it in the 3x3 convolution. This modification results in approximately 0.5% better top-1 accuracy, with a minimal 5% performance trade-off in processing speed.
- Pre-trained on ImageNet-1k dataset
- Optimized for 224x224 image inputs
- Implements residual learning architecture
- Modified stride placement for enhanced accuracy
Core Capabilities
- Image classification across 1,000 ImageNet classes
- Feature extraction for downstream tasks
- Transfer learning applications
- Robust performance on computer vision tasks
Frequently Asked Questions
Q: What makes this model unique?
ResNet-34's uniqueness lies in its implementation of residual learning, which solved the degradation problem in deep neural networks. The v1.5 variant specifically optimizes the stride placement for better accuracy while maintaining efficient processing.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks and can be used as a feature extractor for transfer learning. It's particularly well-suited for applications requiring robust image understanding and classification across diverse categories.
