| # ConvNeXt-Tiny | |
| Run **ConvNeXt-Tiny** on Qualcomm NPU with [nexaSDK](https://sdk.nexa.ai). | |
| ## Quickstart | |
| 1. **Install nexaSDK** and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai) | |
| 2. **Activate your device** with your access token: | |
| ```bash | |
| nexa config set license '<access_token>' | |
| ``` | |
| 3. Run the model locally in one line: | |
| ```bash | |
| nexa infer NexaAI/convnext-tiny-npu | |
| ``` | |
| ## 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. | |
| ConvNeXt-Tiny brings transformer-like accuracy to a purely convolutional design — combining modern architectural updates with the efficiency of classical CNNs. | |
| ## 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. | |
| ## 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 | |
| ## Inputs and Outputs | |
| **Input:** | |
| - RGB image tensor (usually `3 × 224 × 224`) | |
| - Normalized using ImageNet mean and standard deviation | |
| **Output:** | |
| - 1000-dimensional logits for ImageNet class probabilities | |
| - Optional intermediate feature maps when used as a backbone | |
| ## 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` |