ResNet101.tv_in1k Model
| Property | Value |
|---|---|
| Parameter Count | 44.7M |
| License | BSD-3-Clause |
| Paper | Deep Residual Learning for Image Recognition |
| Framework | PyTorch (timm) |
| Image Size | 224x224 |
What is resnet101.tv_in1k?
ResNet101.tv_in1k is a deep residual neural network implementation from the torchvision library, trained on the ImageNet-1k dataset. This model represents a ResNet-B variant that incorporates several architectural improvements over the original ResNet design.
Implementation Details
The model features a sophisticated architecture with 44.7M parameters, utilizing ReLU activations throughout its network. It employs a single-layer 7x7 convolution with pooling at the input stage and uses 1x1 convolution shortcut downsample connections to maintain information flow through the deep network.
- 7.8 GMACs computational complexity
- 16.2M activations
- 224x224 input resolution
- Trained on ImageNet-1k dataset
Core Capabilities
- Image Classification: Optimized for 1000-class ImageNet classification
- Feature Extraction: Can be used as a backbone for various computer vision tasks
- Transfer Learning: Suitable for fine-tuning on domain-specific tasks
- Batch Processing: Efficient handling of batched inputs
Frequently Asked Questions
Q: What makes this model unique?
This model implements the ResNet-B architecture with specific optimizations from torchvision, offering a good balance between accuracy and computational efficiency. With 44.7M parameters, it provides robust feature extraction capabilities while maintaining reasonable inference speeds.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, feature extraction, and transfer learning applications. It performs particularly well in scenarios requiring high-quality image feature representation, such as object detection, image segmentation, and fine-grained classification tasks.
