| # ConvNeXt-Tiny |
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| Run **ConvNeXt-Tiny** on Qualcomm NPU with [nexaSDK](https://sdk.nexa.ai). |
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| ## Quickstart |
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| 1. **Install nexaSDK** and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai) |
| 2. **Activate your device** with your access token: |
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| ```bash |
| nexa config set license '<access_token>' |
| ``` |
| 3. Run the model locally in one line: |
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| ```bash |
| nexa infer NexaAI/convnext-tiny-npu |
| ``` |
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| ## Model Description |
| **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). |
| 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 |
| - **High-accuracy Image Classification**: Pretrained on ImageNet-1K with strong top-1 accuracy. |
| - **Flexible Backbone**: Commonly used as a feature extractor for detection, segmentation, and multimodal systems. |
| - **Optimized for Efficiency**: Compact model size enables fast inference and low latency on CPUs, GPUs, and NPUs. |
| - **Modernized CNN Design**: Adopts ViT-inspired improvements such as layer normalization, larger kernels, and inverted bottlenecks. |
| - **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 |
| - Real-time image recognition on edge or mobile devices |
| - Vision backbone for multimodal and perception models |
| - Visual search, tagging, and recommendation systems |
| - Transfer learning and fine-tuning for domain-specific tasks |
| - Efficient deployment in production or research environments |
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| ## Inputs and Outputs |
| **Input:** |
| - RGB image tensor (usually `3 × 224 × 224`) |
| - Normalized using ImageNet mean and standard deviation |
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| **Output:** |
| - 1000-dimensional logits for ImageNet class probabilities |
| - Optional intermediate feature maps when used as a backbone |
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| ## License |
| - 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. |
| - Commercial licensing or usage rights must be obtained through a separate agreement. For inquiries regarding commercial use, please contact `dev@nexa.ai` |
