| --- |
| 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): |
|
|
|  |
|  |
|
|
| 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): |
|  |
|  |
|
|
