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--- |
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license: cc-by-4.0 |
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--- |
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# ConvNeXt-Tiny |
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Run **ConvNeXt-Tiny** on Qualcomm NPU with [nexaSDK](https://sdk.nexa.ai). |
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## Model Description |
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**ConvNeXt-Tiny** is a lightweight convolutional neural network (CNN) developed by Meta AI, designed to modernize traditional ConvNet architectures with design principles inspired by Vision Transformers (ViTs). |
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With around **28 million parameters**, it achieves competitive ImageNet performance while remaining efficient for on-device and edge inference. |
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ConvNeXt-Tiny brings transformer-like accuracy to a purely convolutional design — combining modern architectural updates with the efficiency of classical CNNs. |
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## Features |
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- **High-accuracy Image Classification**: Pretrained on ImageNet-1K with strong top-1 accuracy. |
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- **Flexible Backbone**: Commonly used as a feature extractor for detection, segmentation, and multimodal systems. |
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- **Optimized for Efficiency**: Compact model size enables fast inference and low latency on CPUs, GPUs, and NPUs. |
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- **Modernized CNN Design**: Adopts ViT-inspired improvements such as layer normalization, larger kernels, and inverted bottlenecks. |
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- **Scalable Family**: Part of the ConvNeXt suite (Tiny, Small, Base, Large, XLarge) for different compute and accuracy trade-offs. |
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## Use Cases |
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- Real-time image recognition on edge or mobile devices |
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- Vision backbone for multimodal and perception models |
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- Visual search, tagging, and recommendation systems |
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- Transfer learning and fine-tuning for domain-specific tasks |
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- Efficient deployment in production or research environments |
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## Inputs and Outputs |
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**Input:** |
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- RGB image tensor (usually `3 × 224 × 224`) |
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- Normalized using ImageNet mean and standard deviation |
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**Output:** |
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- 1000-dimensional logits for ImageNet class probabilities |
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- Optional intermediate feature maps when used as a backbone |
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## License |
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- All NPU-related components of this project — including code, models, runtimes, and configuration files under the src/npu/ and models/npu/ directories — are licensed under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) license. |
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- Commercial licensing or usage rights must be obtained through a separate agreement. For inquiries regarding commercial use, please contact `dev@nexa.ai` |
