ResNet18.tv_in1k Model
| Property | Value |
|---|---|
| Parameter Count | 11.7M |
| License | BSD-3-Clause |
| Framework | PyTorch/timm |
| Paper | Deep Residual Learning for Image Recognition |
| Top-1 Accuracy | 69.76% |
What is resnet18.tv_in1k?
ResNet18.tv_in1k is a ResNet-B architecture model designed for image classification tasks. It's the TorchVision implementation of the original ResNet-18 architecture, trained on the ImageNet-1k dataset. This model represents a balanced approach between computational efficiency and performance, featuring 11.7M parameters and achieving 69.76% top-1 accuracy.
Implementation Details
The model implements the ResNet-B architecture with several key technical features:
- ReLU activation functions throughout the network
- Single layer 7x7 convolution with pooling
- 1x1 convolution shortcut downsample paths
- Input image size of 224x224 pixels
- 1.8 GMACs computational requirement
- 2.5M activation parameters
Core Capabilities
- Image Classification: Primary function for 1000-class ImageNet classification
- Feature Extraction: Can be used as a backbone for downstream tasks
- Embedding Generation: Capable of producing image embeddings for various applications
- Real-time Processing: Relatively lightweight architecture suitable for production deployments
Frequently Asked Questions
Q: What makes this model unique?
This model represents the standard TorchVision implementation of ResNet-18, offering a good balance between model size and performance. It's particularly notable for its efficient architecture and widespread compatibility with PyTorch ecosystems.
Q: What are the recommended use cases?
The model is well-suited for general image classification tasks, transfer learning applications, and as a feature extractor for computer vision pipelines. It's particularly effective when computational resources are limited but reasonable performance is required.
