Project Frigg

--- tags: - depth-estimation - monocular-depth - anydepth - dinov2 - anima - robotics license: apache-2.0 library_name: pytorch pipeline_tag: depth-estimation --- # FRIGG — AnyDepth Depth Estimation **Paper**: [AnyDepth: Depth Estimation Made Easy](https://arxiv.org/abs/2601.02760) **Module**: FRIGG (ANIMA Wave 6) **Architecture**: DINOv2-Base (frozen) + SDT Head (trained) ## Model Details - **Encoder**: DINOv2-ViT-B/14 (86.6M params, frozen) - **Decoder**: SDT Head (9.5M params, trained) - **Total**: ~96M params (only 9.5M trainable) - **Input**: RGB image (518x518 optimal) - **Output**: Dense depth map ## Exported Formats | 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 | ## Usage ```python import torch from anima_frigg.models import build_anydepth, load_dinov2_backbone backbone = load_dinov2_backbone(model_path="pytorch/frigg_v1.pth") model = build_anydepth(backbone) model.load_state_dict(torch.load("pytorch/frigg_v1.pth")) image = torch.randn(1, 3, 518, 518).cuda() depth = model(image) # (1, 1, H, W) ``` ## Training - **Datasets**: NYUv2 (1449 samples) + KITTI (7481 pairs) - **Loss**: SILog + Gradient Matching - **Optimizer**: AdamW (lr=1e-4) - **Hardware**: 4x NVIDIA L4 23GB (DDP) ## Citation ```bibtex @article{ren2026anydepth, title={AnyDepth: Depth Estimation Made Easy}, author={Ren, Zeyu and Zhang, Zeyu and Li, Wukai and Liu, Qingxiang and Tang, Hao}, year={2026} } ``` Generated: 2026-03-31T16:39:28.292515

Source context: 0 downloads · 0 likes · Pipeline depth-estimation · Library pytorch · Repo ilessio-aiflowlab/project_frigg