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README.md

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+# 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 `[email protected]`