GENet Large (gernet_l.idstcv_in1k)
| Property | Value |
|---|---|
| Parameter Count | 31.2M |
| Model Type | Image Classification |
| License | Apache-2.0 |
| Paper | Neural Architecture Design for GPU-Efficient Networks |
| Framework | PyTorch (timm) |
What is gernet_l.idstcv_in1k?
GENet Large is a GPU-efficient neural network architecture designed specifically for image classification tasks. It represents a sophisticated balance between computational efficiency and model performance, implemented through timm's flexible BYOB (Bring-Your-Own-Blocks) Network framework. With 31.2M parameters and operating on 256x256 images, it achieves efficient processing while maintaining high accuracy on ImageNet-1k classification tasks.
Implementation Details
The model is built using timm's BYOB architecture, which provides exceptional flexibility in network design. It features customizable block layouts, configurable stem structures, and adjustable output stride with dilation options.
- Flexible block and stage layout configuration
- Customizable activation and normalization layers
- Support for channel and spatial/self-attention layers
- Integration of stochastic depth and gradient checkpointing
- Layer-wise learning rate decay capability
- Per-stage feature extraction functionality
Core Capabilities
- High-performance image classification on ImageNet-1k dataset
- Feature map extraction with multiple resolution outputs
- Image embedding generation for downstream tasks
- GPU-optimized architecture for efficient processing
- Support for both inference and feature extraction workflows
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its GPU-efficient architecture design, offering an optimal balance between computational resources and performance. It's particularly notable for its implementation using timm's BYOB framework, which provides extensive customization options while maintaining efficiency.
Q: What are the recommended use cases?
The model is best suited for image classification tasks, feature extraction, and generating image embeddings. It's particularly effective for applications requiring efficient GPU utilization while maintaining high accuracy, such as large-scale image processing systems and real-time classification tasks.
