README.md

metadata

license: apache-2.0
base_model: depth-anything/Depth-Anything-V2-Metric-Hypersim-Small
tags:
  - depth-estimation
  - depth2depth

depth2depth model files

The model files the depth2depth crate builds into itself. They are Depth Anything V2 Small, metric, Hypersim (indoor, max depth 20 m), converted from the official depth_anything_v2_metric_hypersim_vits.pth:

• dinov2_vits14.safetensors, da2_head_vits.safetensors: the encoder and the DPT head, for candle (converted by tools/convert_weights.py; includes the unused ImageNet classifier head candle's dinov2 loads).
• da2_metric_hypersim_vits.onnx: an ONNX export taking any input size that is a multiple of 14, for TensorRT (made by the crate's tools/export_onnx.py). da2_metric_hypersim_vits_364x448.onnx is the older fixed-size one.
• Prebuilt fp16 TensorRT engines for a 448x560 input (dimos's default), so these GPUs skip the minutes-long engine build: ..._448x560_trt100700_sm87.engine (TensorRT 10.7, Jetson AGX Orin, JetPack 6 / L4T R36.3) and ..._448x560_trt101601_sm120.engine (TensorRT 10.16, RTX 50-series). Other TensorRT versions, GPUs or sizes reject them and the crate builds its own from the ONNX. (..._364x448_trt100700_sm87.engine is an older fixed-size one.)

The crate pins these files by sha256 in its model.json. Weights are Apache-2.0, as the upstream Small model.