Depth Anything V2 Large — SafeTensors

Depth Anything V2 (Large, ViT-L backbone) converted to SafeTensors format for safe, fast loading in robotic depth estimation pipelines. 335M parameters for high-quality monocular depth maps.

This model is part of the RobotFlowLabs model library, built for the ANIMA agentic robotics platform — a modular ROS2-native AI system that brings foundation model intelligence to real robots operating in the real world.

Why This Model Exists

Monocular depth estimation is fundamental to robotic navigation and manipulation — robots need to know how far away things are from a single camera. Depth Anything V2 produces the highest-quality relative depth maps from a single image. The original weights are distributed as raw .pth files. We converted them to SafeTensors format for safe, zero-copy memory-mapped loading.

Model Details

Property	Value
Architecture	DPT head + ViT-Large encoder
Parameters	335M
Encoder	ViT-L/14 (DINOv2-based)
Input Resolution	Flexible (recommended 518×518)
Output	Dense relative depth map
Training	Synthetic + real depth labels (multi-stage)
Original Model	`depth-anything/Depth-Anything-V2-Large`
License	Apache-2.0

Included Files

depth-anything-v2-large/
├── model.safetensors          # 1.3 GB — Full model weights
└── README.md                  # This file

Quick Start

from safetensors.torch import load_file
import torch

# Load SafeTensors weights
state_dict = load_file("model.safetensors")

# Load into Depth Anything V2 architecture
from depth_anything_v2.dpt import DepthAnythingV2

model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024])
model.load_state_dict(state_dict)
model.to("cuda").eval()

# Predict depth
depth = model.infer_image(image)  # Returns relative depth map

With Transformers

from transformers import AutoModelForDepthEstimation, AutoImageProcessor
import torch

processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Large")
model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Large")
model.to("cuda").eval()

inputs = processor(images=image, return_tensors="pt").to("cuda")
with torch.no_grad():
    depth = model(**inputs).predicted_depth

With FORGE (ANIMA Integration)

from forge.vision import VisionEncoderRegistry

depth_estimator = VisionEncoderRegistry.load("depth-anything-v2-large")
depth_map = depth_estimator(image_tensor)  # Relative depth map

Use Cases in ANIMA

Depth estimation is critical across ANIMA modules:

Obstacle Avoidance — Real-time depth maps for safe navigation
Grasp Planning — Estimate object distance for manipulation reach calculations
3D Reconstruction — Dense depth for point cloud generation from single camera
Safety Zones — Distance-based safety boundaries for human-robot collaboration
Path Planning — Identify traversable spaces and obstacle heights

Depth Anything V2 Family

Model	Params	Size	Best For
depth-anything-v2-large	335M	1.3 GB	Highest quality depth
depth-anything-v2-small	24.8M	95 MB	Real-time edge deployment

Intended Use

Designed For

Monocular depth estimation for robotic navigation
Dense depth maps for manipulation planning
Point cloud generation from RGB cameras
Obstacle detection and distance estimation

Limitations

Produces relative (not metric) depth — requires calibration for absolute distances
Performance degrades on reflective, transparent, or textureless surfaces
Single-frame estimation — no temporal consistency for video
Inherits biases from training data distribution

Out of Scope

Safety-critical autonomous driving without additional validation
Medical depth estimation
Surveillance applications

Attribution

Original Model: depth-anything/Depth-Anything-V2-Large by TUM & HKU
License: Apache-2.0
Paper: Depth Anything V2 — Yang et al., 2024
Converted by: RobotFlowLabs using FORGE

Citation

@article{yang2024depth_anything_v2,
  title={Depth Anything V2},
  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
  journal={arXiv preprint arXiv:2406.09414},
  year={2024}
}

Built with FORGE by RobotFlowLabs
Optimizing foundation models for real robots.