inception_v3.tf_adv_in1k
| Property | Value |
|---|---|
| Parameter Count | 23.9M |
| Model Type | Image Classification |
| Architecture | Inception-v3 |
| License | Apache-2.0 |
| Paper | Rethinking the Inception Architecture |
What is inception_v3.tf_adv_in1k?
The inception_v3.tf_adv_in1k is an adversarially trained variant of the Inception-v3 architecture, specifically designed for robust image classification. Originally developed in TensorFlow and ported to PyTorch by Ross Wightman, this model combines the powerful Inception-v3 architecture with adversarial training techniques to enhance its resilience against adversarial attacks.
Implementation Details
This model implementation features a sophisticated architecture requiring 299x299 input images and utilizes 23.8M parameters with 5.7 GMACs. The model's architecture includes multiple inception modules and has been specifically optimized for the ImageNet-1k dataset.
- Input Resolution: 299x299 pixels
- Parameters: 23.9M
- GMACs: 5.7
- Activations: 9.0M
Core Capabilities
- Image Classification: Robust classification across 1000 ImageNet categories
- Feature Extraction: Capable of generating feature maps at various scales
- Embedding Generation: Can produce 2048-dimensional image embeddings
- Adversarial Robustness: Enhanced resistance to adversarial attacks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its adversarial training approach, making it more robust against potential attacks while maintaining high classification accuracy. It's particularly valuable for applications where security and reliability are crucial.
Q: What are the recommended use cases?
The model is ideal for production environments requiring robust image classification, feature extraction for downstream tasks, and scenarios where resistance to adversarial attacks is important. It's particularly well-suited for security-sensitive applications and research in adversarial machine learning.
