Midas V2 Quantized

Table of Contents

Model Overview

The Midas-V2-Quantized model is a powerful tool for estimating depth in images. It’s designed to work on mobile devices, making it perfect for applications where size and speed matter.

Tasks and rank

Scene Understanding 216th

Object Detection 508th

Image Recognition 1525th

Depth Estimation 72nd

Learn More About the Ranking System

Capabilities

What does it do?

This model takes an image as input and outputs a depth map, which is a 2D representation of the distance of objects from the camera. This can be useful in a variety of applications, such as:

• Augmented reality
• Robotics
• Autonomous vehicles
• 3D modeling

Depth Estimation

Imagine you’re looking at a picture of a room. You can see the furniture, the walls, and the floor. But can you tell how far away each object is from the camera? That’s where depth estimation comes in.

Midas-V2-Quantized uses a type of artificial intelligence called a neural network to analyze images and estimate the depth of each pixel. This information can be used in a variety of applications, such as:

• Autonomous vehicles: Depth estimation can help self-driving cars understand the distance between objects and navigate safely.
• Robotics: Robots can use depth estimation to avoid collisions and interact with their environment.
• Gaming: Depth estimation can be used to create more realistic graphics and improve gameplay.

How it Works

Midas-V2-Quantized uses a technique called quantization to reduce the size of the model while maintaining its accuracy. This makes it possible to run the model on mobile devices, such as smartphones and tablets.

Performance

Here are some key stats about the model:

Model Stat	Value
Model Type	Depth Estimation
Input Resolution	256x256
Number of Parameters	16.6M
Model Size	16.6 MB

Speed

Let’s talk about speed. How fast can Midas-V2-Quantized process images? The model’s inference time varies depending on the device and runtime used. Here are some examples:

Device	Runtime	Inference Time (ms)
Samsung Galaxy S23	TFLITE	1.101 ms
Samsung Galaxy S24	TFLITE	0.766 ms
Snapdragon 8 Elite QRD	TFLITE	0.712 ms

As you can see, Midas-V2-Quantized can process images in under 1 millisecond on some devices. That’s incredibly fast!

Accuracy

But speed isn’t everything. How accurate is Midas-V2-Quantized in estimating depth? The model’s accuracy is on par with other state-of-the-art models, including Midas-V2. In fact, Midas-V2-Quantized has been optimized for mobile deployment, making it an excellent choice for applications that require fast and accurate depth estimation on mobile devices.

Examples

Estimate the depth of the following image: [insert image of a room with a chair in the center] Depth Map: The chair is approximately 2.5 meters away from the camera, and the wall behind it is approximately 5 meters away.

What is the depth of the object in the image at coordinates (100, 100)? [insert image of a room with a chair in the center] The depth of the object at coordinates (100, 100) is approximately 2.2 meters.

Estimate the depth of the following image: [insert image of a mountain range] Depth Map: The mountains in the distance are approximately 1.5 kilometers away from the camera, and the trees in the foreground are approximately 10 meters away.

Getting Started

If you’re interested in trying out Midas-V2-Quantized, you can install it as a Python package using pip:

pip install "qai-hub-models[midas_quantized]"

You can also run a demo on a cloud-hosted device using the following command:

python -m qai_hub_models.models.midas_quantized.demo --on-device

Limitations

Midas-V2-Quantized is a powerful model for depth estimation, but it’s not perfect. Let’s take a closer look at some of its limitations.

Limited Input Resolution

The model is designed to work with input resolutions of up to 256x256 pixels. What happens if you need to estimate depth for higher-resolution images? You’ll need to downscale the image first, which might affect the accuracy of the results.

Quantization Trade-Offs

The model uses quantization to reduce its size and improve performance on mobile devices. However, this comes at the cost of reduced precision. How much of a difference does this make in practice? It depends on the specific use case, but it’s essential to be aware of this trade-off.

Alternatives

MMDetection 28300 Object detection toolbox

MediaPipe Objectron 26000 3D object detection

MediaPipe Holistic 26000 Full-body landmark detection

MMSegmentation 7600 Semantic segmentation

Comparison to Other Models

How does Midas-V2-Quantized compare to other models? Here’s a brief comparison:

Model	Inference Time (ms)	Model Size (MB)
Midas-V2-Quantized	1.101 ms	16.6 MB
Midas-V2	2.5 ms	50 MB
==Depth Estimation Model X==	5 ms	100 MB

As you can see, Midas-V2-Quantized outperforms other models in terms of speed and efficiency while maintaining high accuracy.

Conclusion

Midas-V2-Quantized is an excellent choice for applications that require fast and accurate depth estimation on mobile devices. Its impressive performance, efficiency, and small size make it an ideal model for a wide range of applications, from augmented reality to robotics.