tf_efficientnetv2_s.in21k
| Property | Value |
|---|---|
| Parameter Count | 48.3M |
| License | Apache-2.0 |
| Training Dataset | ImageNet-21k |
| Paper | EfficientNetV2: Smaller Models and Faster Training |
What is tf_efficientnetv2_s.in21k?
tf_efficientnetv2_s.in21k is an optimized implementation of the EfficientNetV2-S architecture, originally trained in TensorFlow and successfully ported to PyTorch by Ross Wightman. This model represents a significant advancement in efficient deep learning, trained on the extensive ImageNet-21k dataset.
Implementation Details
The model features 48.2M parameters with 5.4 GMACs and 22.8M activations. It operates on image sizes of 300x300 during training and 384x384 for testing, demonstrating impressive efficiency in processing visual data.
- Optimized architecture for improved training speed and efficiency
- Supports both classification and feature extraction workflows
- Implements advanced training techniques from the EfficientNetV2 paper
- Compatible with PyTorch ecosystem through timm library
Core Capabilities
- Image Classification with high accuracy on ImageNet-21k classes
- Feature Map Extraction for downstream tasks
- Image Embedding Generation
- Transfer Learning applications
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency improvements of EfficientNetV2 architecture with comprehensive training on ImageNet-21k, making it particularly suitable for transfer learning and general-purpose computer vision tasks. It offers an excellent balance between model size and performance.
Q: What are the recommended use cases?
The model excels in image classification tasks, feature extraction for downstream applications, and generating image embeddings. It's particularly valuable for applications requiring transfer learning or working with large-scale image datasets.
