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| Format | File | Use Case |
|---|---|---|
| PyTorch (.pth) | pytorch/frigg_v1.pth | Training, fine-tuning |
| SafeTensors | pytorch/frigg_v1.safetensors | Fast loading, safe |
| ONNX | onnx/frigg_v1.onnx | Cross-platform inference |
| TensorRT FP16 | tensorrt/frigg_v1_fp16.trt | Edge deployment (Jetson/L4) |
| TensorRT FP32 | tensorrt/frigg_v1_fp32.trt | Full precision inference |
1import torch
2from anima_frigg.models import build_anydepth, load_dinov2_backbone
3
4backbone = load_dinov2_backbone(model_path="pytorch/frigg_v1.pth")
5model = build_anydepth(backbone)
6model.load_state_dict(torch.load("pytorch/frigg_v1.pth"))
7
8image = torch.randn(1, 3, 518, 518).cuda()
9depth = model(image) # (1, 1, H, W)1@article{ren2026anydepth,
2 title={AnyDepth: Depth Estimation Made Easy},
3 author={Ren, Zeyu and Zhang, Zeyu and Li, Wukai and Liu, Qingxiang and Tang, Hao},
4 year={2026}
5}