mtbui2010/depth-anything-v2-small-ONNX

Depth Anything V2 Small — ONNX with dynamic input size

ONNX export of depth-anything/Depth-Anything-V2-Small-hf (Apache-2.0) for VisionServe:

visionserve pull depth-anything-v2

Only the Small variant is published: Depth Anything V2 Base and Large are CC-BY-NC-4.0.

Why another export

The input is [1, 3, H, W] with dynamic H and W, so the image can be fed at the size the reference DPTImageProcessor uses (keep the aspect ratio, scale by whichever of 518/W, 518/H is closer to 1, each side rounded to a multiple of 14, bicubic). For example, an 848×480 photo is fed at 910×518 instead of being squashed to 518×518.

A plain dynamic-axes export of this model is wrong. The legacy tracer bakes the 518×518 output size into the head (int(patch_h * 14) in modeling_depth_anything.py), so every input returns a 518×518 map. This export keeps that size traced, and it is checked against PyTorch at three input shapes (518×518, 910×518 and 518×686).

I/O contract

	name	shape
input	pixel_values	[1, 3, H, W] f32	H, W multiples of 14; /255, ImageNet mean/std
output	predicted_depth	[1, H, W] f32	relative inverse depth (disparity), unnormalised

Verification (VisionServe converter, 7 non-square photos)

check	result
ONNX vs PyTorch, 3 input shapes	max |Δ|/scale 1.05e-05
served preprocessing vs DPTImageProcessor	mean 0.25 grey levels, same shapes on 7/7 images
served depth map vs predicted_depth	Pearson r min 0.9999, mean 1.000

License

Apache-2.0, inherited from depth-anything/Depth-Anything-V2-Small-hf. The weights are not modified.