ConvNeXt XXLarge CLIP LAION2B
| Property | Value |
|---|---|
| Parameters | 846.5M |
| License | Apache 2.0 |
| Image Size | 256x256 |
| Top-1 Accuracy | 88.61% |
| GMACs | 198.1 |
What is convnext_xxlarge.clip_laion2b_soup_ft_in1k?
This is a state-of-the-art ConvNeXt model that represents the evolution of convolutional neural networks for computer vision tasks. Initially pretrained on the massive LAION-2B dataset using CLIP training, then fine-tuned on ImageNet-1k, it achieves exceptional performance while maintaining practical efficiency.
Implementation Details
The model leverages the ConvNeXt architecture, incorporating modern deep learning advances while maintaining the simplicity of traditional CNNs. With 846.5M parameters, it processes 256x256 images using 198.1 GMACs, delivering a balance of accuracy and computational efficiency.
- Highly efficient architecture with 124.5M activations
- CLIP-style pretraining on LAION-2B dataset
- Fine-tuned specifically for ImageNet-1k classification
- Supports various input modes including feature extraction and embedding generation
Core Capabilities
- Image Classification with 88.61% top-1 accuracy
- Feature Map Extraction across multiple scales
- Image Embedding Generation for downstream tasks
- Efficient batch processing with 256 samples per second
Frequently Asked Questions
Q: What makes this model unique?
This model combines the scale of LAION-2B pretraining with the efficiency of ConvNeXt architecture, achieving top-tier performance (88.61% accuracy) while maintaining practical inference speeds.
Q: What are the recommended use cases?
The model excels in high-accuracy image classification tasks, feature extraction for downstream applications, and generating image embeddings for various computer vision applications. It's particularly suitable for scenarios requiring both high accuracy and reasonable computational resources.
