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project_frigg
ilessio-aiflowlab/project_frigg
project_frigg is a depth estimation model from ilessio-aiflowlab. Use it for the depth estimation task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
Paper: AnyDepth: Depth Estimation Made Easy Module: FRIGG (ANIMA Wave 6) Architecture: DINOv2-Base (frozen) + SDT Head (trained)
Access
Public
Updated Mar 31, 2026
Repo size
6.2 GB
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Repo makeup
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.pth844 MB · 38%
At a glance
- Task
- Depth Estimation
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Mar 31, 2026
- SHA
- b793071a
Model card
From the Hugging Face model README
FRIGG — AnyDepth Depth Estimation
Paper: AnyDepth: Depth Estimation Made Easy 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
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
@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
- Task
- Depth Estimation
- Library
- pytorch
- License
- apache-2.0
- Created
- Mar 28, 2026
- Updated
- Mar 31, 2026