ConvNeXt Base Model (Facebook AI)
| Property | Value |
|---|---|
| Parameter Count | 88.6M |
| License | Apache 2.0 |
| Architecture | ConvNeXt Base |
| Training Data | ImageNet-22k, fine-tuned on ImageNet-1k |
| Paper | A ConvNet for the 2020s |
What is convnext_base.fb_in22k_ft_in1k?
This is a state-of-the-art convolutional neural network model developed by Facebook AI Research. It represents a modern reimagining of the traditional ConvNet architecture, designed to compete with transformer-based models while maintaining the efficiency of convolution operations.
Implementation Details
The model features 88.6M parameters and operates with GMACs of 15.4. It processes images at 224x224 pixels during training and 288x288 for testing. The architecture leverages advanced training techniques including ImageNet-22k pretraining followed by ImageNet-1k fine-tuning.
- Optimized for modern deep learning workflows
- Supports both classification and feature extraction
- Implements efficient batch processing
- Uses F32 tensor operations
Core Capabilities
- Image Classification with high accuracy
- Feature Map Extraction across multiple scales
- Image Embedding generation
- Supports both inference and training modes
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of traditional CNNs with modern architectural improvements, achieving strong performance while maintaining computational efficiency. Its dual-stage training on ImageNet-22k and ImageNet-1k provides robust feature representation.
Q: What are the recommended use cases?
The model excels in image classification tasks, feature extraction for downstream tasks, and generating image embeddings for various computer vision applications. It's particularly suitable for scenarios requiring a balance between accuracy and computational efficiency.
