ConvNeXt V2 Tiny
| Property | Value |
|---|---|
| Parameter Count | 28.6M |
| License | Apache 2.0 |
| Architecture | ConvNeXt V2 |
| Task | Image Classification |
| Paper | View Paper |
What is convnextv2-tiny-1k-224?
ConvNeXt V2 tiny is a state-of-the-art convolutional neural network developed by Facebook for image classification tasks. This particular model represents the compact version trained on ImageNet-1K dataset, operating at 224x224 resolution. It implements the innovative FCMAE (Fully Convolutional Masked Autoencoder) framework and introduces a Global Response Normalization (GRN) layer.
Implementation Details
The model employs a pure convolutional architecture, eschewing transformer components while maintaining competitive performance. It's implemented in PyTorch and supports F32 tensor operations.
- Utilizes FCMAE framework for enhanced feature learning
- Incorporates Global Response Normalization (GRN) layer
- Trained on ImageNet-1K dataset
- Optimized for 224x224 image inputs
Core Capabilities
- High-accuracy image classification across 1000 ImageNet classes
- Efficient inference with 28.6M parameters
- Supports batch processing and real-time classification
- Compatible with standard image processing pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its pure convolutional approach while achieving transformer-level performance through the innovative FCMAE framework and GRN layer. It offers an excellent balance between efficiency and accuracy.
Q: What are the recommended use cases?
The model is ideal for production image classification tasks, particularly when working with standard resolution images (224x224). It's suitable for applications requiring robust classification across the ImageNet category space while maintaining reasonable computational requirements.
