> For the complete documentation index, see [llms.txt](https://nx.docs.scailable.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nx.docs.scailable.net/ai-manager-v6.1.4/ai-models-support/importing-models/common-models.md).

# Common Models

The Nx toolkit currently supports image classification and object detection models. Guides are available for deploying common models such as MobileNet, ResNet, EfficientNet, ViT, and LeViT for image classification, and YoloS, Yolov4, Yolov7, and Yolov11 for object detection.

Key steps involved in this process include:

1. Incorporating necessary post-processing steps such as masking and a configurable Non-Maximum Suppression (NMS) for object detection models and Softmax for image classification models directly into the ONNX graph.
2. Modifying the model's input and output shapes and names as per the requirements of the AI Manager.
3. Making sure the exported ONNX has an Operator Set Version up to 17.

These guides give AI developers off-the-shelf scripts to deploy these models within minutes. This is achieved by exporting the models to ONNX format, ensuring compatibility with the AI Manager.

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You can find the guides on this [Github repository](https://github.com/scailable/nxai-model-to-onnx).
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