furiosa-models 0.10.2

Furiosa Models

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.

Releases

• v0.10.2 - 2024-05-29
• v0.10.1 - 2023-11-25
• v0.10.0 - 2023-08-28
• v0.9.1 - 2023-05-26
• v0.9.0 - 2023-05-12
• v0.8.0 - 2022-11-10

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:

• Furiosa Models - Latest Documentation
• Model Object
• Model List
• Command Line Tool
• Furiosa SDK - Tutorial and Code Examples

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

• Furiosa Models - Latest Documentation
• Furiosa Models - Github
• Furiosa SDK Documentation