ResNet50.tv_in1k
| Property | Value |
|---|---|
| Parameter Count | 25.6M |
| GMACs | 4.1 |
| Activations | 11.1M |
| Image Size | 224 x 224 |
| Paper | Deep Residual Learning for Image Recognition |
What is resnet50.tv_in1k?
ResNet50.tv_in1k is a powerful implementation of the ResNet-B architecture, specifically designed for image classification tasks. This model represents the torchvision variant of the popular ResNet50 architecture, trained on the ImageNet-1k dataset. It combines deep residual learning with efficient architectural choices to deliver robust performance in computer vision tasks.
Implementation Details
The model implements several key architectural features that define its performance and efficiency:
- ReLU activations throughout the network for non-linear transformations
- Single-layer 7x7 convolution with pooling for initial feature extraction
- 1x1 convolution shortcut downsample connections
- 25.6M trainable parameters optimized for ImageNet classification
- 4.1 GMACs computational requirement for inference
- 11.1M activations during processing
Core Capabilities
- Image classification on 1000 ImageNet classes
- Feature extraction for transfer learning
- Support for 224x224 pixel input images
- Efficient inference with reasonable computational requirements
Frequently Asked Questions
Q: What makes this model unique?
This model represents the official torchvision implementation of ResNet50, offering a proven balance between performance and computational efficiency. Its architecture has been extensively validated and is considered a reliable backbone for many computer vision tasks.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, transfer learning applications, and as a feature extraction backbone for more complex computer vision pipelines. It's particularly effective when working with datasets similar to ImageNet or when fine-tuning for specific domain applications.
