Skip to content

Furiosa Models

furiosa-models is an open model zoo project for FuriosaAI NPU. It provides a set of public pre-trained, pre-quantized models for learning and demo purposes or for developing your applications.

furiosa-models also includes pre-packaged post/processing utilities, and compiler configurations optimized for FuriosaAI NPU. However, all models are standard ONNX or tflite models, and they can run even on CPU and GPU as well.


Online Documentation

If you are new, you can start from Getting Started. You can also find the latest online documents, including programming guides, API references, and examples from the following:

Model List

The table summarizes all models available in furiosa-models. If you visit each model link, you can find details about loading a model, their input and output tensors, pre/post-processing, and usage examples.

Model Task Size Accuracy
ResNet50 Image Classification 25M 75.618% (ImageNet1K-val)
EfficientNetB0 Image Classification 6.4M 72.44% (ImageNet1K-val)
EfficientNetV2-S Image Classification 26M 83.532% (ImageNet1K-val)
SSDMobileNet Object Detection 7.2M mAP 0.232 (COCO 2017-val)
SSDResNet34 Object Detection 20M mAP 0.220 (COCO 2017-val)
YOLOv5M Object Detection 21M mAP 0.272 (Bdd100k-val) [1]
YOLOv5L Object Detection 46M mAP 0.284 (Bdd100k-val) [1]
YOLOv7w6Pose Pose Estimation 80M N/A

[1]: The accuracy of the yolov5 f32 model trained with bdd100k-val dataset, is mAP 0.295 (for yolov5m) and mAP 0.316 (for yolov5l).

See Also