Depth Anything 3

• Original code: https://github.com/ByteDance-Seed/Depth-Anything-3 at 3d835ec
• Code licence: Apache-2.0 (permissive); files: LICENSE
• Weights licence: Apache-2.0
  • da3-large-1.1: CC-BY-NC-4.0
  • da3-giant-1.1: CC-BY-NC-4.0
  • da3nested-giant-large-1.1: CC-BY-NC-4.0
• Licence evidence: https://github.com/ByteDance-Seed/Depth-Anything-3/blob/main/README.md (Model Cards table) and the Hugging Face model cards (DA3-LARGE-1.1's card says Apache 2.0, the repository table CC BY-NC 4.0; the table is recorded)
• Training data: Public academic datasets only (model cards); teacher-student training on synthetic and real multi-view data per the paper
• Licence note: The Mono and Metric weights are Apache-2.0 but their training data terms are unchecked (a note only); the other variants' weights are CC-BY-NC-4.0 (no).
• Options: long_side = 504 (>= 14; also process_res)
• Kernel libraries: fastbb, dpt_fused
• Variants: da3mono-large, da3metric-large, da3-large-1.1, da3-giant-1.1, da3nested-giant-large-1.1
• Outputs: depth, confidence, intrinsics, mask, sky (in extras); kind: metric
• Input size: any H x W >= 8, resized or padded inside the model (ViT patch 14)
• Tested up to (portable, fp16, RTX 5060 Ti 16 GB): da3mono-large 3000x4000 (1.9 GiB peak allocated, 2.1 reserved); da3metric-large 3000x4000 (1.9 GiB peak allocated, 2.1 reserved); da3-large-1.1 3000x4000 (1.9 GiB peak allocated, 2.2 reserved); da3-giant-1.1 3000x4000 (5.1 GiB peak allocated, 5.4 reserved); da3nested-giant-large-1.1 3000x4000 (6.3 GiB peak allocated, 6.7 reserved)
• da3mono-large 994x1482, first call: portable + torch.compile 25 s (cold Inductor cache), TensorRT fp16 52 s (ONNX + engine).
• da3mono-large original (fp32) 994x1482: float32 (the authors' autocast off)
• da3mono-large original 994x1482: original default (api.py): torch.autocast(bfloat16)
• da3metric-large original (fp32) 994x1482: float32 (the authors' autocast off)
• da3metric-large original 994x1482: original default (api.py): torch.autocast(bfloat16)
• da3-large-1.1 original (fp32) 994x1482: float32 (the authors' autocast off)
• da3-large-1.1 original 994x1482: original default (api.py): torch.autocast(bfloat16)
• da3mono-large load / first call: original 2.96 s / 0.282 s, portable 4.25 s / 0.092 s, kernels 1.44† s / 0.018 s
• da3metric-large load / first call: original 2.7 s / 0.283 s, portable 0.35 s / 0.049 s, kernels 0.3† s / 0.016 s
• da3-large-1.1 load / first call: original 3.46 s / 0.45 s, portable 3.65 s / 0.25 s, kernels 0.31† s / 0.027 s
• da3-giant-1.1 load / first call: original 12.39 s / 0.684 s, portable 6.29 s / 0.163 s, kernels 0.9† s / 0.061 s
• da3nested-giant-large-1.1 load / first call: original 28.67 s / 1.626 s, portable 13.25 s / 0.548 s, kernels 2.73† s / 0.07 s
• † measured in the speed sweep after the portable load (or an earlier size) in the same process: file cache and kernels are warm, so the time is lower than a fresh start.
• Protocol, median of 5 after 3 warm-up, uint8 CUDA input (synthetic for bench_inputs sizes), card defaults; gc before each load; peak = max over the first call and the timed calls

Measured

AbsRel / delta1 after a least-squares scale + shift fit (in disparity space for inverse-depth models), mean over images. Original = the original code named under Details, same checkpoint and protocol.

Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 468fba10, 2026-11-05 to 2026-11-06.

Parity

CPU, float32: max |diff| <= 0.005 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.5 % and p99 <= 2 % of the reference's median |value| (absolute error of the output depth).