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IGEV Plusplus

This version of RT IGEV has been converted to run on the Axera NPU using w8a16 quantization.

ArtOtherVersionRevision b5e505fBBewertung

Modellquelle

Art
Other
Dateigröße
29,3–40,5 MB
Version
vRevision b5e505f
Aktualisiert
20. Okt. 2026
Downloads
14
Quelle
Hugging Face

Quellenbeschreibung

IGEV++

This version of RT IGEV has been converted to run on the Axera NPU using w8a16 quantization.

Compatible with Pulsar2 version: 5.0-patch1

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through

Support Platform

ChipsModelsTime
AX650AX650_RTIGEV139.803 ms
AX637AX637_RTIGEV385.72 ms

How to use

Download all files from this repository to the device

python env requirement

pyaxengine

https://github.com/AXERA-TECH/pyaxengine

wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc2/axengine-0.1.3-py3-none-any.whl
pip install axengine-0.1.3-py3-none-any.whl

others

Maybe None.

Inference with AX650 Host (such as M4N-Dock, a.k.a. 爱芯派 Pro) or AX637 Host

Input image (take 1 pair of images as examples):

Left image Right image

run:

# the default target_chip is AX650, the default input is all images in `demo-img` folder
python3 infer.py 

Output image:

AXmodel result (take 2 images as examples): AX650 output image example AX637 output image example

Quellen

1 Quelle
Hugging FacePrimärVersion b5e505fcaeaecdcee9bae2959d29c1f1ebec08a5
huggingface.co/AXERA-TECH/IGEV-plusplus/tree/b5e505fcaeaecdcee9bae2959d29c1f1ebec08a5

Verifiziert 20. Okt.

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Modellartefakte

2 Artefakte

models/AX637_quant_axmodel.onnx

onnx · 40,5 MB · SHA-256 731ad7e39c38…5486 · Hugging Face

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models/AX650.onnx

onnx · 29,3 MB · SHA-256 a6aa8792a40f…66ae · Hugging Face

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Quellenauszüge

3 Auszüge

--- license: bsd-3-clause language: - en pipeline_tag: depth-estimation tags: - IGEV++ --- # IGEV++ This version of RT IGEV has been converted to run on the Axera NPU using **w8a16** quantization. Compatible with Pulsar2 version: 5.0-patch1 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through - [The repo of original](https://github.com/gangweiX/IGEV-plusplus) - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - AX8850 - [M.2 Accelerator card](https://docs.m5stack.com/en/ai_hardware/LLM-8850_Card) |Chips|Models |Time| |--|--|--| |AX650|AX650_RTIGEV| 139.803 ms | |AX637|AX637_RTIGEV| 385.72 ms | ## How to use Download all files from this repository to the device ### python env requirement #### pyaxengine https://github.com/AXERA-TECH/pyaxengine ``` wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc2/axengine-0.1.3-py3-none-any.whl pip install axengine-0.1.3-py3-none-any.whl ``` #### others Maybe None. #### Inference with AX650 Host (such as M4N-Dock, a.k.a. 爱芯派 Pro) or AX637 Host Input image (take 1 pair of images as examples): ![Left image](./demo-imgs/Adirondack/im0.png) ![Right image](./demo-imgs/Adirondack/im1.png) run: ```sh # the default target_chip is AX650, the default input is all images in `demo-img` folder python3 infer.py ``` Output image: AXmodel result (take 2 images as examples): ![AX650 output image example](./output-imgs/AX650/AXModel_Disparity_Map-rt-Adirondack.png) ![AX637 output image example](./output-imgs/AX637/AXModel_Disparity_Map-rt-2.png)

Source context: 14 downloads · 0 likes · Pipeline depth-estimation · Repo AXERA-TECH/IGEV-plusplus

Source context: 9 downloads · 0 likes · Pipeline depth-estimation · Repo AXERA-TECH/IGEV-plusplus