EfficientNet-Lite0
| Property | Value |
|---|---|
| Parameters | 4.7M |
| GMACs | 0.4 |
| Input Size | 224x224 |
| Dataset | ImageNet-1k |
| Paper | EfficientNet: Rethinking Model Scaling |
What is tf_efficientnet_lite0.in1k?
tf_efficientnet_lite0.in1k is a lightweight variant of the EfficientNet architecture, specifically designed for mobile and edge devices. Originally developed in TensorFlow by the paper authors and later ported to PyTorch by Ross Wightman, this model represents an optimal balance between computational efficiency and accuracy for image classification tasks.
Implementation Details
The model features a carefully optimized architecture with 4.7M parameters and requires only 0.4 GMACs for inference. It operates on 224x224 pixel images and produces 6.7M activations during processing. The implementation supports various usage modes including classification, feature extraction, and embedding generation.
- Efficient architecture with mobile-optimized design
- Pre-trained on ImageNet-1k dataset
- Support for feature map extraction at multiple scales
- Flexible embedding extraction capabilities
Core Capabilities
- Image Classification: Direct classification with softmax probabilities
- Feature Extraction: Multiple-scale feature maps for downstream tasks
- Embedding Generation: 1280-dimensional image embeddings
- Transfer Learning: Pre-trained weights for fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient design that maintains good performance while requiring minimal computational resources, making it ideal for mobile and edge deployments. It achieves this through careful architecture optimization and the EfficientNet scaling principles.
Q: What are the recommended use cases?
The model is particularly well-suited for mobile applications, edge devices, and scenarios where computational resources are limited. It's effective for image classification tasks, feature extraction, and as a backbone for transfer learning in computer vision applications.
