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Depth Anything V2 - Monocular Depth Estimation

Depth Anything V2 is a state-of-the-art monocular depth estimation model that predicts depth maps from single RGB images.

Overview

Depth Anything V2 uses a vision transformer architecture to estimate relative depth from images. Key capabilities include:

  • Monocular Depth Estimation - Predict depth from a single image
  • High Quality - State-of-the-art performance on depth estimation benchmarks
  • Multiple Model Sizes - Choose from small, base, or large variants
  • Zero-Shot Generalization - Works on diverse image types without fine-tuning

License & Attribution

License: Apache 2.0
Source: Depth-Anything-V2
Paper: Depth Anything V2

Pre-trained Model IDs

Depth Anything V2 pre-trained models are available and require a Roboflow API key.

Model ID Description
depth-anything-v2/small Small model - fastest inference
depth-anything-v2/base Base model - balanced performance
depth-anything-v2/large Large model - best accuracy
depth-anything-v2 Alias for depth-anything-v2/small

Supported Backends

Backend Extras Required
hf (Hugging Face) Included in base installation

Roboflow Platform Compatibility

Feature Supported
Training ❌ Not available for training
Upload Weights ❌ Not supported
Serverless API (v2) ✅ Deploy via hosted API
Workflows ✅ Use in Workflows via Depth Anything block
Edge Deployment (Jetson) ⚠️ Limited support (potential VRAM issues)
Self-Hosting ✅ Deploy with inference-models (GPU recommended)

Usage Examples

Basic Depth Estimation

import cv2
import numpy as np
from inference_models import AutoModel

# Load model (requires API key)
model = AutoModel.from_pretrained("depth-anything-v2/small", api_key="your_roboflow_api_key")

# Load image
image = cv2.imread("path/to/image.jpg")

# Predict depth
depth_maps = model(image)
depth_map = depth_maps[0].cpu().numpy()

# Normalize for visualization
depth_normalized = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
depth_colored = cv2.applyColorMap((depth_normalized * 255).astype(np.uint8), cv2.COLORMAP_INFERNO)

# Save visualization
cv2.imwrite("depth_map.jpg", depth_colored)

Processing Multiple Images

import cv2
from inference_models import AutoModel

# Load model
model = AutoModel.from_pretrained("depth-anything-v2/base", api_key="your_roboflow_api_key")

# Load multiple images
images = [cv2.imread(f"image_{i}.jpg") for i in range(5)]

# Batch inference
depth_maps = model(images)

# Process each depth map
for i, depth_map in enumerate(depth_maps):
    depth_np = depth_map.cpu().numpy()
    print(f"Image {i}: depth shape = {depth_np.shape}")

Using the Large Model

import cv2
from inference_models import AutoModel

# Load larger model for better accuracy
model = AutoModel.from_pretrained("depth-anything-v2/large", api_key="your_roboflow_api_key")

# Load image
image = cv2.imread("path/to/image.jpg")

# Predict depth
depth_maps = model(image)
depth_map = depth_maps[0]

print(f"Depth map shape: {depth_map.shape}")
print(f"Depth range: [{depth_map.min():.2f}, {depth_map.max():.2f}]")

Workflows Integration

Depth Anything V2 can be used in Roboflow Workflows for complex computer vision pipelines. The Depth Anything block outputs depth maps that can be used for 3D reconstruction, scene understanding, and more.

Learn more: Workflows Documentation

Performance Tips

  1. Use GPU - Depth estimation models require GPU for acceptable performance
  2. Choose the right model - Use small for speed, large for accuracy
  3. Batch processing - Process multiple images together when possible
  4. Normalize outputs - Depth maps are relative; normalize for visualization or comparison

Output Format

The model returns a list of PyTorch tensors, one per input image: - Shape: (height, width) - same as input image dimensions - Values: Relative depth values (not metric depth) - Range: Varies per image; normalize for visualization

Common Use Cases

  • 3D Reconstruction - Estimate scene geometry from images
  • Augmented Reality - Understand scene depth for AR applications
  • Robotics - Navigation and obstacle avoidance
  • Photography - Depth-based effects and bokeh simulation
  • Scene Understanding - Analyze spatial relationships in images