---
title: convnext-tiny-npu
canonical_url: "https://www.modelscope.cn/models/NexaAIDev/convnext-tiny-npu"
md_url: "https://www.modelscope.cn/models/NexaAIDev/convnext-tiny-npu.md"
repository: NexaAIDev/convnext-tiny-npu
last_updated: 2026-01-12
model_type:
  - convnext
architectures:
  - ConvNextForImageClassification
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 137
stars: 0
---

# convnext-tiny-npu

> convnext-tiny-npu - NexaAIDev 在 ModelScope 开源的模型。Run ConvNeXt-Tiny on Qualcomm NPU with nexaSDK.

- **Repository**: NexaAIDev/convnext-tiny-npu
- **Downloads**: 137
- **Stars**: 0
- **Last updated**: 2026-01-12

Source: https://www.modelscope.cn/models/NexaAIDev/convnext-tiny-npu

---

# 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`