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).