NexaAI/convnext-tiny-npu

ConvNeXt-Tiny

Run ConvNeXt-Tiny on Qualcomm NPU with nexaSDK.

Quickstart

1. Install nexaSDK and create a free account at sdk.nexa.ai

2. Activate your device with your access token:

   nexa config set license '<access_token>'
   

3. Run the model locally in one line:

   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]