README.md

---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: image-classification
tags:
- AIoT
- QNN
---

![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F%25E5%259B%25BE-17.png&w=640&q=75)

## ResNet-34: Image Classification

ResNet-34 is a deep convolutional neural network in the ResNet (Residual Network) family, introduced by Kaiming He and colleagues in 2015. ResNet-34 consists of 34 layers and effectively addresses the vanishing gradient problem in deep networks by introducing residual (skip) connections, making the network easier to train as depth increases. These residual connections allow input features to bypass several layers, reducing training difficulty and enhancing performance. With greater depth than ResNet-18, ResNet-34 maintains a relatively low parameter count, making it suitable for tasks balancing computational efficiency and accuracy. This model is widely used in computer vision tasks like image classification, object detection, and semantic segmentation.

### Source model

- Input shape: 224x224
- Number of parameters: 20.79M
- Model size: 83.14M
- Output shape: 1x1000

Source model repository: [resnet](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py)

## Performance Reference

Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)

## Inference & Model Conversion

Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)

## License

- Source Model: [BSD-3-CLAUSE](https://github.com/pytorch/vision/blob/main/LICENSE)

- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf)