Depth Anything

depth-anything

Depth Anything is highly practical model for robust monocular depth estimation by training on a combination of 1.5M labeled images and 62M+ unlabeled images

L40S 45GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~2 sec

StatusActive

Version

0.0.1

Updated20 days ago

Live Demo

Average runtime: ~2 seconds

Input

Configure model parameters

encoder

An enumeration.

Image

Input image

File upload is currently disabled

JPEGPNGJPGWEBP

Output

View generated results

Result

Preview, share or download your results with a single click.

Cost is calculated based on execution time.The model is charged at $0.0011 per second. With a $1 budget, you can run this model approximately 454 times, assuming an average execution time of 2 seconds per run.

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "depth-anything",
            "version": "0.0.1",
            "input": {
  "image": "your_file.image/jpeg",
  "encoder": "vitl"
}
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~2 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Depth Anything is a highly adaptable model designed for generating depth maps from 2D images. By analyzing image features with sophisticated encoders, the model translates visual data into structured depth representations. It is ideal for tasks requiring spatial understanding, 3D reconstruction, or depth-based analysis across diverse industries.

Technical Specifications

Depth Mapping: Converts 2D images into structured depth maps for analysis and visualization with Depth Anything.

Multi-Encoder Compatibility: Supports several encoder options (vits, vtb, and vitl) for varying levels of detail and speed.

Scalable Design: Performs consistently well across images of different resolutions.

Generalization Ability: Adaptable to a variety of image types, making it useful for tasks like 3D reconstruction, scene understanding, and robotics with Depth Anything.

Key Considerations

Input Quality: Poor-quality images (e.g., low resolution, noise, heavy compression) can negatively impact depth map accuracy.

Complex Scenes: In images with overlapping or heavily occluded objects, depth estimation may require post-processing for improved clarity.

Lighting Variations: Extreme lighting conditions, such as shadows or overexposure, can introduce inaccuracies in depth mapping.

Tips & Tricks

Image Input for Depth Anything

Use high-resolution, well-lit images for the clearest depth mapping.
Avoid heavy compression or noisy artifacts, as these can degrade output accuracy.
Ensure clear separation between foreground and background elements in the image.

Encoder Parameter

vits:
- Use for tasks where speed is critical (e.g., real-time processing).
- Best for small-scale images or applications with limited computational resources.
vtb:
- Provides a balance between processing speed and depth map quality.
- Works well for most general-purpose tasks.
vitl:
- Use for complex scenes or images where the highest detail is essential.
- Recommended for high-resolution inputs where precise spatial understanding is required.

General Tips for Depth Anything

Consistency: When processing multiple images, use the same encoder across all images for uniform results.
Batch Preprocessing: Normalize image size and quality across datasets to maintain output consistency.
Post-Processing: Refine output maps using edge enhancement or smoothing filters for polished depth representations.
Lighting Adjustments: For dimly lit images, pre-process by enhancing brightness and contrast before input.

Capabilities

Depth Map Generation: Creating depth maps for scene analysis and reconstruction.
3D Visualization: Providing foundational data for 3D modeling and rendering.
Scene Understanding: Identifying spatial relationships within an image.

What can I use for?

3D Reconstruction: Assisting in creating 3D models from 2D inputs.

AR/VR Development: Enhancing depth perception in augmented and virtual reality applications.

Things to be aware of

Use vitl with high-resolution architectural images for detailed 3D reconstructions.

Process low-light images by pre-enhancing brightness before running the model.

Experiment with edge refinement filters on generated depth maps for sharper visuals.

Test various encoder settings on the same image to observe differences in depth quality and processing time.

Limitations

Occluded Objects: Depth Anything may struggle with objects that are partially or fully obscured. Post-processing techniques can help resolve such issues.

Extreme Lighting: Overexposed or underexposed images may reduce depth estimation accuracy.

Scene Complexity: Highly cluttered or ambiguous scenes might lead to less precise depth maps.

Speed vs. Precision: While vitl delivers exceptional detail, it can increase processing time significantly. Choose encoders wisely based on task requirements.

Output Format: PNG

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