Comparing Background Removal Models: BiRefNet vs rembg vs U2Net
The article compares the performance of three popular background removal models - BiRefNet, rembg, and U2Net - on a dataset of 500 real product images, highlighting their strengths and weaknesses in handling fine details like hair, glass, and transparent objects.
Why it matters
The choice of background removal model can have a significant impact on the quality and cost of production workflows, especially for e-commerce and creative applications that require consistent, high-quality results.
Key Points
- 1BiRefNet outperforms rembg and U2Net in hair accuracy (94% vs 81% and 71%) and handling transparent/glass objects (78% vs 59% and 48%)
- 2rembg and U2Net struggle with fine details, leading to issues like blocky halos and partial disappearance of products
- 3BiRefNet is the state-of-the-art model as of 2025, using high-resolution reference features to preserve edges
- 4Running BiRefNet via an API is easier than setting up rembg or U2Net locally, with no GPU or dependency management required
Details
The article highlights that background removal is a more complex problem than it may seem, with failure cases like hair strands turning into blocky halos, glass objects disappearing, and semi-transparent fabric becoming opaque. The author ran a benchmark of 500 real product images through three popular models - rembg, U2Net, and the state-of-the-art BiRefNet. The results show that BiRefNet significantly outperforms the other two models in handling fine details like hair and transparent objects, with 94% accuracy on hair and 78% on glass/transparent objects, compared to 81% and 59% for rembg, and 71% and 48% for U2Net. While BiRefNet is slightly slower (1.4s vs 1.1s for rembg and 0.8s for U2Net), the author argues that the quality difference is crucial, as the 6% gap in hair accuracy can translate to 30 images per 500 batch needing manual touch-up. The article also compares the ease of use, with BiRefNet offering a simple API-based solution, while rembg and U2Net require more setup and dependency management for local deployment.
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