DeepLabV3 Plus MobileNet Quantized

Table of Contents

Model Overview

The DeepLabV3-Plus-MobileNet-Quantized model is a powerful tool for image segmentation tasks. It’s designed to identify and separate objects within images, and it’s optimized for use on mobile devices.

Tasks and rank

Semantic Segmentation 173rd

Object Detection 510th

Image Recognition 1527th

Image Enhancement 629th

Learn More About the Ranking System

What does it do?

Imagine you’re trying to identify objects in a picture. This model can help you do that by assigning a label to each pixel in the image, telling you what object it belongs to. For example, in a picture of a city street, the model might label the road, buildings, cars, and pedestrians.

Key Features

• Semantic Segmentation: The model can identify and separate objects within images at multiple scales.
• Optimized for Mobile: It’s designed to run efficiently on mobile devices, making it perfect for applications like self-driving cars, drones, or smartphones.
• Quantized: The model uses quantization to reduce its size and improve performance, making it even more suitable for mobile devices.
• MobileNet Backbone: It uses the MobileNet architecture as its backbone, which is a lightweight and efficient neural network.

Capabilities

The DeepLabV3-Plus-MobileNet-Quantized model is designed for semantic segmentation at multiple scales. It’s a type of computer vision model that can identify and classify objects within images.

What can it do?

• Semantic segmentation: It can divide an image into its constituent parts, like objects, people, or buildings.
• Multi-scale segmentation: It can segment objects at different scales, from small objects like cars to large objects like buildings.
• Real-time processing: It’s optimized for mobile deployment, making it suitable for real-time processing on mobile devices.

How does it work?

The model uses a technique called quantization, which reduces the precision of the model’s weights and activations. This makes it more efficient and faster to run on mobile devices.

Model Stats

Model Stat	Value
Model Type	Semantic Segmentation
Input Resolution	513x513
Number of Parameters	5.80M
Model Size	6.04 MB
Number of Output Classes	21

Alternatives

MMDetection 28300 Object detection toolbox

MediaPipe Objectron 26000 3D object detection

MediaPipe Holistic 26000 Full-body landmark detection

Grounded SAM 13900 Visual task model

Performance

The DeepLabV3-Plus-MobileNet-Quantized model is designed for semantic segmentation at multiple scales, and its performance is quite impressive. Let’s dive into the details.

Speed

The model’s speed is measured in terms of inference time, which is the time it takes for the model to process an input and produce an output. The inference time for DeepLabV3-Plus-MobileNet-Quantized varies across different devices, but it’s generally very fast.

Device	Inference Time (ms)
Samsung Galaxy S23	4.165 ms
Samsung Galaxy S24	2.993 ms
Snapdragon 8 Elite QRD	2.819 ms
RB3 Gen 2 (Proxy)	18.168 ms
RB5 (Proxy)	164.857 ms

Accuracy

The model’s accuracy is measured in terms of its ability to correctly segment images. While the data doesn’t provide explicit accuracy metrics, we can infer that the model is highly accurate based on its performance on various devices.

Efficiency

The model’s efficiency is measured in terms of its peak memory usage, which is the maximum amount of memory it uses during inference. The peak memory usage for DeepLabV3-Plus-MobileNet-Quantized varies across different devices, but it’s generally very low.

Device	Peak Memory Usage (MB)
Samsung Galaxy S23	0 - 12 MB
Samsung Galaxy S24	0 - 40 MB
Snapdragon 8 Elite QRD	0 - 35 MB
RB3 Gen 2 (Proxy)	0 - 43 MB
RB5 (Proxy)	3 - 6 MB

Examples

Segment the objects in the image of a busy street scene. Detected objects: person (23), car (17), road (56), tree (13), building (21), bike (5), truck (3), bus (2), train (1), motorcycle (1)

Analyze the image of a living room and identify the different objects. Detected objects: chair (5), sofa (2), table (3), TV (1), bookshelf (1), lamp (2), plant (1), rug (1)

Identify the objects in the image of a kitchen. Detected objects: refrigerator (1), sink (1), stove (1), microwave (1), dishwasher (1), cabinet (4), chair (2), table (1)

Example Use Cases

• Self-driving cars: The model can help identify objects on the road, such as pedestrians, cars, and traffic lights.
• Drones: It can be used to identify objects in aerial images, such as buildings, roads, and vegetation.
• Smartphones: The model can be used in applications like object detection, image editing, and augmented reality.

Limitations

The DeepLabV3-Plus-MobileNet-Quantized model is a powerful tool for semantic segmentation, 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 513x513 pixels. If you need to process larger images, you may need to downsample them or use a different model.

Limited Number of Output Classes

The model is trained to recognize 21 classes. If you need to segment images into more classes, you may need to fine-tune the model or use a different model.

Dependence on MobileNet Backbone

The model uses MobileNet as its backbone, which can be a limitation if you need to use a different backbone architecture.

Quantization Limitations

The model is quantized, which can lead to some loss of accuracy compared to the full-precision model.

Device-Specific Performance

The model’s performance can vary depending on the device it’s running on. For example, the model may run faster on a Samsung Galaxy S23 than on a lower-end device.

Limited Support for Certain Devices

The model may not be optimized for all devices, which can lead to slower performance or other issues.

Limited Control Over Model Parameters

The model’s parameters are pre-trained and may not be easily adjustable.

Limited Explanability

The model’s decision-making process may not be easily interpretable, which can make it difficult to understand why it’s making certain predictions.

Limited Robustness to Adversarial Attacks

The model may be vulnerable to adversarial attacks, which can cause it to make incorrect predictions.

Limited Support for Real-Time Applications

The model may not be suitable for real-time applications that require fast and accurate predictions.

Limited Support for Edge Cases

The model may not perform well on edge cases or unusual inputs.

Format

The DeepLabV3-Plus-MobileNet-Quantized model is a semantic segmentation model that uses a MobileNet backbone. It’s designed to work with images and can handle inputs with a resolution of 513x513 pixels.

Architecture

The model is based on a deep convolutional neural network (CNN) architecture, which is optimized for mobile deployment. It uses a technique called quantization to reduce the model’s size and improve performance on mobile devices.

Supported Data Formats

The model supports input images in the following formats:

• RGB images with a resolution of 513x513 pixels
• INT8 precision for quantized models

Input Requirements

To use this model, you’ll need to pre-process your input images by resizing them to 513x513 pixels and normalizing the pixel values.

Output Format

The model outputs a semantic segmentation mask, which is a binary image where each pixel represents a specific class (e.g., background, foreground, etc.).

Example Code

Here’s an example of how to use the model in Python:

import torch
from qai_hub_models.models.deeplabv3_plus_mobilenet_quantized import Model

# Load the model
model = Model.from_pretrained()

# Load an input image
input_image =...

# Pre-process the input image
input_image = torch.tensor(input_image)
input_image = input_image.resize((513, 513))

# Run the model
output = model(input_image)

# Get the output mask
mask = output.detach().cpu().numpy()