| Property | Value |
|---|---|
| Parameters | 44.7M |
| License | Apache-2.0 |
| Training Data | ImageNet-1k |
| Top-1 Accuracy | 82.8% |
| Paper | ResNet Strikes Back |
What is resnet101.a1h_in1k?
ResNet101.a1h_in1k is a powerful image classification model based on the ResNet architecture, specifically optimized using the "ResNet Strikes Back" methodology. It represents a significant advancement in deep learning for computer vision, featuring 44.7M parameters and achieving 82.8% top-1 accuracy on ImageNet-1k.
Implementation Details
The model implements the ResNet-B architecture with several key optimizations:
- Single layer 7x7 convolution with pooling
- 1x1 convolution shortcut downsample
- ReLU activations throughout the network
- LAMB optimizer for training
- Enhanced dropout and stochastic depth
- Advanced RandAugment data augmentation
Core Capabilities
- High-performance image classification on 1000 classes
- Feature extraction capabilities for transfer learning
- Efficient inference with 7.8 GMACs
- Flexible input resolution (224px training, 288px inference)
Frequently Asked Questions
Q: What makes this model unique?
This model combines the proven ResNet architecture with modern training techniques from the "ResNet Strikes Back" paper, achieving strong performance while maintaining reasonable computational requirements.
Q: What are the recommended use cases?
The model excels in general image classification tasks, transfer learning applications, and as a backbone for more complex computer vision tasks like object detection or segmentation.