Inception ResNet v2
| Property | Value |
|---|---|
| Parameter Count | 55.9M |
| Model Type | Image Classification |
| Architecture | Inception-ResNet Hybrid |
| Input Size | 299 x 299 |
| License | Apache 2.0 |
| Paper | View Paper |
What is inception_resnet_v2.tf_in1k?
The Inception-ResNet-v2 is a sophisticated deep learning model that combines the innovative Inception architecture with residual connections. Originally developed by Google researchers, this model represents a significant advancement in computer vision, boasting 55.8M parameters and optimized for ImageNet classification tasks.
Implementation Details
This implementation is a PyTorch port from the original TensorFlow model, featuring 13.2 GMACs computational complexity and 25.1M activations. The model processes images at 299x299 resolution and utilizes a hybrid architecture that leverages both Inception modules and residual connections for enhanced performance.
- Specialized feature extraction capabilities with multiple output scales
- Supports both classification and embedding generation
- Implements efficient multi-scale feature processing
Core Capabilities
- Image Classification with 1000 classes (ImageNet)
- Feature map extraction at various scales
- Embedding generation for transfer learning
- Support for both inference and training workflows
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines Inception modules with residual connections, offering superior feature extraction capabilities while maintaining computational efficiency. The hybrid architecture allows for better gradient flow and feature reuse compared to traditional CNNs.
Q: What are the recommended use cases?
The model excels in image classification tasks, particularly those requiring fine-grained feature extraction. It's well-suited for transfer learning, computer vision research, and production deployments where accuracy is crucial. The model can be used for classification, feature extraction, or as a backbone for more complex vision tasks.
