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]