VGG19 Torchvision ImageNet Model
| Property | Value |
|---|---|
| Parameter Count | 143.7M |
| License | BSD-3-Clause |
| Research Paper | Very Deep Convolutional Networks for Large-Scale Image Recognition |
| Dataset | ImageNet-1k |
| Image Size | 224 x 224 |
What is vgg19.tv_in1k?
VGG19.tv_in1k is a powerful implementation of the VGG19 architecture, specifically trained on the ImageNet-1k dataset. This model represents a significant achievement in deep learning, featuring 143.7M parameters and optimized for high-accuracy image classification tasks.
Implementation Details
The model operates on 224x224 pixel images and employs a deep convolutional architecture with 19.6 GMACs computational requirements. It's built using the PyTorch framework and includes Safetensors support for efficient tensor operations.
- 19 layers deep architecture with systematic organization
- Optimized for both classification and feature extraction
- Supports various input processing modes including feature map extraction
- Includes pre-trained weights from torchvision
Core Capabilities
- Image Classification with 1000-class ImageNet categories
- Feature Map Extraction with multiple resolution outputs
- Image Embedding Generation for transfer learning
- Flexible architecture supporting various downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
This model combines the robust VGG19 architecture with torchvision's official weights, offering excellent performance for both classification and feature extraction. Its 143.7M parameters provide comprehensive feature representation capabilities.
Q: What are the recommended use cases?
The model excels in image classification tasks, feature extraction for transfer learning, and as a backbone for computer vision applications. It's particularly suitable for scenarios requiring high-quality image feature representation.
