ResNet152.a1h_in1k Model
| Property | Value |
|---|---|
| Parameter Count | 60.3M |
| License | Apache 2.0 |
| Top-1 Accuracy | 82.8% |
| GMACs | 11.6 |
| Paper | ResNet Strikes Back |
What is resnet152.a1h_in1k?
ResNet152.a1h_in1k is a powerful implementation of the ResNet architecture, specifically designed for image classification tasks. Based on the "ResNet Strikes Back" methodology, this model represents a significant advancement in the ResNet family, incorporating modern training techniques and optimizations.
Implementation Details
This model features a sophisticated architecture with 60.3M parameters, utilizing the LAMB optimizer and enhanced training procedures. It processes images at 224x224 resolution for training and 288x288 for testing, achieving an impressive 82.8% top-1 accuracy on ImageNet-1k.
- ReLU activations throughout the network
- Single layer 7x7 convolution with pooling
- 1x1 convolution shortcut downsample
- Enhanced dropout and stochastic depth
- Advanced RandAugment implementation
Core Capabilities
- High-performance image classification
- Feature extraction capabilities with multiple output stages
- Efficient inference with 11.6 GMACs
- Flexible input resolution support
- Robust training stability with cosine learning rate schedule
Frequently Asked Questions
Q: What makes this model unique?
This model combines the classic ResNet-152 architecture with modern training techniques from the "ResNet Strikes Back" paper, featuring enhanced augmentation and optimization strategies that result in superior performance compared to the original ResNet-152.
Q: What are the recommended use cases?
The model excels in image classification tasks, feature extraction for downstream tasks, and as a backbone for more complex computer vision applications. It's particularly suitable for scenarios requiring a balance between accuracy and computational efficiency.
