root@ax650:~/AXERA-TECH/Depth-Anything-V2# tree
.
|-- README.md
|-- calib-cocotest2017.tar
|-- config.json
|-- depth_anything_v2_vits.onnx
|-- depth_anything_v2_vits_ax620e.axmodel
|-- depth_anything_v2_vits_ax650.axmodel
|-- examples
|   |-- demo01.jpg
....
|   `-- demo20.jpg
|-- output-ax.png
`-- python
    |-- infer.py
    |-- infer_onnx.py
    |-- output.png
    `-- requirements.txt

2 directories, 31 files
root@ax650:~/AXERA-TECH/Depth-Anything-V2#

python env requirement

pyaxengine

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

wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/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(爱芯派Pro)

Input image:

root@ax650:~/AXERA-TECH/Depth-Anything-V2# python3 python/infer.py --model depth_anything_v2_vits_ax650.axmodel --img examples/demo01.jpg
[INFO] Available providers:  ['AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 3.3 ae03a08f
root@ax650:~/AXERA-TECH/Depth-Anything-V2# ls

Output image:

Inference with M.2 Accelerator card

Input image:

(base) axera@raspberrypi:~/lhj/Depth-Anything-V2 $ ./axcl_aarch64/axcl_depth_anything -m depth_anything_v2_vits_ax650.axmodel -i examples/demo01.jpg -g 518,518
--------------------------------------
model file : depth_anything_v2_vits_ax650.axmodel
image file : examples/demo01.jpg
img_h, img_w : 518 518
--------------------------------------
axclrtEngineCreateContextt is done. 
axclrtEngineGetIOInfo is done. 

grpid: 0

input size: 1
    name:    input 
        1 x 518 x 518 x 3


output size: 1
    name:   output 
        1 x 1 x 518 x 518

==================================================

Engine push input is done. 
--------------------------------------
post process cost time:7.07 ms 
--------------------------------------
Repeat 1 times, avg time 33.25 ms, max_time 33.25 ms, min_time 33.25 ms

Output image: