burn_depth 🔥📐😎

burn depth pro model inference

input	metric depth

usage

use burn::prelude::*;
use burn_depth::{InferenceBackend, model::depth_pro::DepthPro};

let device = <InferenceBackend as Backend>::Device::default();

let model = DepthPro::<InferenceBackend>::load(&device, "assets/model/depth_pro.mpk")?;

// Image tensor with shape [1, 3, H, W] (batch, channels, height, width)
let input: Tensor<InferenceBackend, 4> = Tensor::zeros([1, 3, 512, 512], &device);

let result = model.infer(input, None);
// result.depth: Tensor<InferenceBackend, 3> with shape [1, H, W]
// result.focallength_px: Tensor<InferenceBackend, 1> with shape [1]

switching between depth_pro and depth anything 3

cargo run --example inference -- \
  --model depth-pro \
  --checkpoint assets/model/depth_pro.mpk \
  --image assets/image/test.jpg

cargo run --example inference -- \
  --model depth-anything3 \
  --checkpoint assets/model/da3_metric_large.mpk \
  --image assets/image/test.jpg

setup

• download depth_pro.pt to assets/model/

• cargo run --bin import_depth_pro --features import

• download da3_metric_large.safetensors to assets/model/

• cargo run --bin import_da3 --features import

• cargo run --example inference -- --help