Depth Anything V2 - AI Image Tools Tool

Overview

Depth Anything V2 is an interactive Hugging Face Space that uses deep learning to generate dense depth maps from single 2D images. The web interface lets users upload an image and receive a per-pixel depth estimate suitable for creative post-processing, 3D compositing, or as a pre-processing step for other computer vision tasks. According to the Space page on Hugging Face, the project has received community interest (606 likes as of the Space listing) but does not publish explicit model parameter counts or a formal performance benchmark on the listing (Hugging Face Space: depth-anything/Depth-Anything-V2). The tool is aimed at artists, developers, and researchers who need quick monocular depth extraction without setting up a local model. Typical workflows include generating depth for image relighting, parallax and 3D effects, depth-aware selection/matting, and feeding depth maps into downstream pipelines for segmentation or scene reconstruction. Because the Space is hosted on Hugging Face, it provides a low-friction way to experiment with depth maps before integrating a production-grade model locally.

Model Statistics

• Likes: 606

Model Details

Depth Anything V2 performs monocular (single-image) depth estimation to produce dense, per-pixel depth maps. Monocular depth estimation is a class of deep learning models that predict relative distance from RGB input, returning a single-channel depth image where pixel intensity corresponds to predicted depth. The Hugging Face Space page does not disclose the exact model architecture, training dataset, parameter counts, or inference pipeline used by the hosted app (pipeline and parameter details are listed as unknown on the Space listing). Therefore, while the Space delivers depth outputs, the underlying network (for example, whether it uses MiDaS, DPT, LeReS, or a custom model) and training specifics are not published there. For users who want a locally runnable alternative, common open-source monocular depth models include MiDaS/DPT (Intel ISL) and LeReS; these reproduce the same kind of dense depth maps produced by interactive spaces like Depth Anything V2 (see MiDaS GitHub for reference). Input/Output: The Space accepts standard RGB images (JPEG/PNG). Output is a dense depth map (single-channel) suitable for export and use in image-editing or CV workflows. Runtime characteristics (latency, batch processing support) are not specified on the Space page.

Key Features

• Interactive Hugging Face Space web UI for uploading images and getting depth outputs
• Generates dense per-pixel depth maps from single RGB images
• Outputs suitable for creative 3D effects, parallax, and depth-aware editing
• Useful as a preprocessing step for segmentation, compositing, or scene reconstruction
• Low-friction experimentation without local model installation

Example Usage

Example (python):

import cv2
import torch
import numpy as np

# Example: generate a depth map locally using MiDaS (alternative to using the Space).
# Requires: pip install torch torchvision opencv-python
# Reference: https://github.com/intel-isl/MiDaS

model_type = "MiDaS_small"  # smaller, faster model
midas = torch.hub.load("intel-isl/MiDaS", model_type)
midas.to('cpu')
midas.eval()

transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
transform = transforms.small_transform

img = cv2.imread("input.jpg")[:, :, ::-1]  # BGR->RGB
input_batch = transform(img).unsqueeze(0)

with torch.no_grad():
    prediction = midas(input_batch)
    prediction = torch.nn.functional.interpolate(
        prediction.unsqueeze(1), size=img.shape[:2], mode="bicubic", align_corners=False
    ).squeeze()

depth = prediction.cpu().numpy()
# Normalize for visualization
depth_min, depth_max = depth.min(), depth.max()
depth_vis = (255 * (depth - depth_min) / (depth_max - depth_min)).astype(np.uint8)

cv2.imwrite("depth_visualization.png", depth_vis)
# Save raw depth as 32-bit float (optional)
depth.astype(np.float32).tofile('depth_raw.f32')

print("Depth map saved: depth_visualization.png")

Benchmarks

Hugging Face likes: 606 (Source: https://huggingface.co/spaces/depth-anything/Depth-Anything-V2)

Hugging Face downloads: 0 (space listing shows 0 downloads) (Source: https://huggingface.co/spaces/depth-anything/Depth-Anything-V2)

Key Information

• Category: Image Tools
• Type: AI Image Tools Tool