seresnextaa101d_32x8d.sw_in12k_ft_in1k
| Property | Value |
|---|---|
| Parameter Count | 93.8M |
| License | Apache-2.0 |
| Top-1 Accuracy | 86.72% |
| Image Size | 224x224 (train) / 288x288 (test) |
What is seresnextaa101d_32x8d.sw_in12k_ft_in1k?
This is a sophisticated image classification model that combines SE-ResNeXt architecture with anti-aliasing and channel attention mechanisms. It represents a significant advancement in convolutional neural network design, incorporating Squeeze-and-Excitation blocks for adaptive feature recalibration and Rectangle-2 Anti-Aliasing for improved shift invariance.
Implementation Details
The model is built on the ResNeXt architecture with several key enhancements:
- 3-layer stem of 3x3 convolutions with pooling
- Grouped 3x3 bottleneck convolutions (32 groups, width=8d)
- Squeeze-and-Excitation channel attention mechanisms
- 2x2 average pool + 1x1 convolution shortcut downsample
- ReLU activations throughout the network
Core Capabilities
- High accuracy image classification (86.72% top-1 on ImageNet)
- Feature extraction for downstream tasks
- Robust performance with 93.8M parameters
- Efficient processing with 17.2 GMACs for 224x224 images
Frequently Asked Questions
Q: What makes this model unique?
This model combines three powerful concepts: ResNeXt's grouped convolutions, Squeeze-and-Excitation attention, and anti-aliasing techniques. It was pretrained on ImageNet-12k and fine-tuned on ImageNet-1k, resulting in superior performance compared to standard architectures.
Q: What are the recommended use cases?
The model excels in high-accuracy image classification tasks, feature extraction for transfer learning, and as a backbone for complex computer vision tasks like object detection or segmentation. It's particularly suitable for applications requiring high accuracy and shift invariance.
