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3 min read

Bria's New State-of-the-Art Remove Background 2.0 Outperforms the Competition

Bria's New State-of-the-Art Remove Background 2.0 Outperforms the Competition
Bria's New State-of-the-Art Remove Background 2.0 Outperforms the Competition
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Intro

In the rapidly evolving field of AI-driven image processing, background removal remains one of the most challenging tasks, particularly in workflows that involve complex image compositions.

This advanced open-source model enables development teams to streamline and scale processes with precise and reliable background removal. By integrating RMBG 2.0 into their workflows, teams can automate intricate image processing tasks, facilitating scalable content creation across various industries such as e-commerce, image editing, stock media, gaming, and more.

 

What’s New in RMBG 2.0

Bria's RMBG 2.0+ sets a new standard in open-source background removal by overcoming the limitations of RMBG 1.4. While the original model was effective, RMBG 2.0+ advances its capabilities to deliver state-of-the-art, industry-leading results that distinguish it from competitors.

RMBG 2.0+ provides unmatched precision in background removal with exceptional accuracy and consistency, even in complex scenes. It is available through a Model, API, and iframe for commercial use, with a commercial agreement required. For non-commercial use, it is only accessible through the model.

 

Impact on Bria API Routes

Separating and layering a “foreground” from a ”background” is the foundation of many complex AI Tasks.  Thus, such an upgrade systematically improves any downstream endpoints utilizing this core function.

 
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See How it Compares to the Competition.

See how our new state-of-the-art 2.0 Background Removal capability consistently outperforms competitors like BiRefNe, Photoshop, and Remove.bg, setting a new benchmark for quality and commercial readiness.

We tested these capabilities and measured their outputs for commercial readiness and quality of results.

rmbg 2.0 blog - compare - 1Three top industry-leading Remove Background, Open Source Models.,

 

 

 

Competitor Overview

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RMBG 2.0


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It offers Remove Background 2.0, which has an open-source model, API, and iFrame. An open-source model was used, and it was the latest open SOTA Photoshop Background Removal offers an Interface and API Company specializing in Background Removal, which is available via a web app and an API

 

 
 

 

Methodology

To evaluate RMBG 2.0+ against other leading background removal models, we benchmarked its performance on diverse images, assessing each model’s output for commercial readiness. Results were rated on a four-point scale: Very Bad for outputs with significant artifacts, Bad for conceptually accurate but unusable results, Good for outputs needing minor adjustments, and Very Good for near-perfect, ready-to-use segmentation.

Our test included diverse subject compositions (e.g., people, objects, animals, text) and photorealistic and non-photorealistic images to gauge versatility. We used images with simple, complex, and transparent backgrounds to test precision and analyzed foreground complexity with single and multiple elements.

This approach gave us a comprehensive view of how RMBG 2.0+ performs across various real-world scenarios, solidifying its position as a state-of-the-art model for consistent, high-quality background removal.

 

Benchmarking Results


This graph illustrates the percentage of benchmarks in which each model achieved usable results, defined as a score of "Good" or "Very Good."

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  • BiRefNet: Bria’s new RMBG 2.0 model outperforms the current open-source SOTA, BiRefNet, by a significant margin (90% vs. 85%), positioning RMBG 2.0 as the new state-of-the-art in open-source background removal.

  • Adobe Photoshop: Bria’s RMBG 2.0 surpasses Adobe Photoshop’s background removal capabilities by a large margin (90% vs. 46%).

  • Remove.bg: Our model delivers competitive results against leading commercial solutions like remove.bg (90% vs. 97%). Particularly in the photorealistic use case, our primary focus, RMBG 2.0, achieved 92% accuracy compared to the removal.bg’s 97%.

  • RMBG 1.4: The new RMBG 2.0 model dramatically improves upon our previous RMBG 1.4 model (90% vs. 74%). This upgrade addresses critical issues in RMBG 1.4, with marked improvements in consistency, reduced indecisiveness, and significantly fewer misclassifications.


This graph compares the percentage of usable results across photorealistic and illustrative backgrounds for each model. Our model demonstrates strong performance, particularly in the photorealistic use case, delivering competitive results with those of remove.bg.

rmbg 2.0 blog - compare -3


This graph displays the score distribution for each model, highlighting RMBG 2.0’s impressive performance. Notably, RMBG 2.0 rarely produces inferior results and predominantly delivers high-quality outputs, achieving a significant proportion of near-perfect results that rival the performance of remove.bg.

rmbg 2.0 blog - compare -4


A critical insight from our analysis is that RMBG 2.0 significantly outperforms BiRefNet in handling complex backgrounds, making it far more suitable for real-world applications.

This graph compares the percentage of usable results on simple versus complex backgrounds across different models. Unlike BiRefNet and RMBG 1.4, RMBG 2.0 demonstrates remarkable robustness on complex backgrounds, consistently delivering reliable and high-quality results.

rmbg 2.0 blog - compare -5

 


Conclusion

The comprehensive benchmarking of RMBG 2.0+ demonstrates its remarkable advancement in background removal, positioning it as the new state-of-the-art solution among open-source models. RMBG 2.0+ consistently outperforms other models, particularly in handling complex backgrounds, where it excels beyond BiRefNet and Adobe Photoshop. Its high accuracy and reliability, especially in photorealistic scenarios, make it highly competitive with commercial solutions like remove.bg.

RMBG 2.0+ addresses critical issues in earlier models, such as inconsistent output and indecisiveness in complex scenes. This upgrade enhances the precision and robustness of Bria’s background removal capabilities and delivers commercial-grade results suitable for diverse real-world applications. Through its open-source availability, RMBG 2.0+ empowers development teams with a reliable, scalable tool for high-quality background removal, setting a new benchmark in the industry.

 

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API Documentation

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Demo Bria’s Remove Background 2.0 Capability in Our Console

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Demo Bria’s Remove Background 2.0 Capability in Hugging Face

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