tf_efficientnetv2_b0.in1k
| Property | Value |
|---|---|
| Parameter Count | 7.1M |
| Model Type | Image Classification |
| License | Apache 2.0 |
| Framework | PyTorch (TimM) |
| Paper | EfficientNetV2: Smaller Models and Faster Training |
What is tf_efficientnetv2_b0.in1k?
This is a PyTorch implementation of the EfficientNetV2-B0 architecture, originally trained in TensorFlow and ported by Ross Wightman. It represents a significant advancement in efficient deep learning models, designed to balance model size and performance. With 7.1M parameters and optimized for both training and inference, it's particularly well-suited for resource-conscious applications.
Implementation Details
The model features a carefully designed architecture utilizing advanced training techniques and optimizations. It operates on 192x192 images during training and 224x224 during testing, with approximately 0.5 GMACs (Giga Multiply-Accumulate Operations) and 3.5M activations.
- Optimized for both speed and efficiency
- Supports feature extraction and embedding generation
- Implements progressive learning strategies
- Provides flexible inference options for different use cases
Core Capabilities
- Image Classification on ImageNet-1k dataset
- Feature map extraction with multiple resolution outputs
- Generation of image embeddings (1280-dimensional feature vectors)
- Support for both classification and feature backbone usage
Frequently Asked Questions
Q: What makes this model unique?
This model represents an optimal balance between efficiency and accuracy, featuring improved training speed and reduced parameter count compared to its predecessors. Its architecture is specifically optimized for modern hardware accelerators while maintaining competitive accuracy.
Q: What are the recommended use cases?
The model is ideal for production environments where efficiency is crucial. It's particularly well-suited for mobile and edge devices, real-time image classification tasks, and as a backbone for transfer learning in custom computer vision applications.
