depth-anything-v2-large

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

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

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}
}

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<p align="center"> <b>Built with FORGE by <a href="https://huggingface.co/robotflowlabs">RobotFlowLabs</a></b><br> Optimizing foundation models for real robots. </p>