tf_efficientnetv2_xl.in21k_ft_in1k
| Property | Value |
|---|---|
| Parameter Count | 208.1M |
| Model Type | Image Classification / Feature Backbone |
| License | Apache-2.0 |
| Image Size | Train: 384x384, Test: 512x512 |
| Paper | EfficientNetV2: Smaller Models and Faster Training |
What is tf_efficientnetv2_xl.in21k_ft_in1k?
This is an advanced implementation of the EfficientNetV2 architecture, specifically the XL variant, that has been pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k. Originally developed in TensorFlow by the paper authors and later ported to PyTorch by Ross Wightman, this model represents a significant advancement in efficient deep learning architectures.
Implementation Details
The model features 208.1M parameters and requires 52.8 GMACs for inference. It operates with a training image size of 384x384 and testing size of 512x512, utilizing 139.2M activations. The architecture has been optimized for both performance and efficiency, making it suitable for high-stakes computer vision tasks.
- Supports multiple usage modes including classification, feature extraction, and embedding generation
- Implements F32 tensor type for precise computations
- Provides comprehensive PyTorch integration through the timm library
Core Capabilities
- Image classification with state-of-the-art accuracy
- Feature map extraction at multiple scales
- Generation of image embeddings for downstream tasks
- Support for both training and inference pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model combines the benefits of being trained on the large-scale ImageNet-21k dataset (14M images) and fine-tuned on ImageNet-1k, providing exceptional transfer learning capabilities and robust feature extraction. Its XL architecture offers superior performance while maintaining reasonable computational requirements.
Q: What are the recommended use cases?
The model is ideal for high-precision image classification tasks, feature extraction for downstream applications, and as a backbone for transfer learning. It's particularly suitable for applications requiring high accuracy and where computational resources are not severely constrained.
