YOLO26-RGB: repurposing YOLO26's depth-trained backbone for image deraining [P]
Our take
![YOLO26-RGB: repurposing YOLO26's depth-trained backbone for image deraining [P]](https://preview.redd.it/iywwsh48kxmh1.png?width=140&height=50&auto=webp&s=d0bd35ce7bd7834bb1b97467da6c05a4b8c904ad)
The recent work repurposing the YOLO26 depth-estimation model for image deraining, detailed in "worldproof: diagnosing where world-model predictions break and a measurement of when pixel metrics stop being able to rank models at all [P," offers a compelling illustration of transfer learning's potential in computer vision. It highlights a surprising efficiency: leveraging a model initially trained for depth perception – a task fundamentally linked to understanding spatial relationships – to significantly improve image deraining performance. This isn't merely about achieving a slightly better result; it’s about demonstrating that architectural components learned in one domain can provide a surprisingly robust foundation for another, particularly when both tasks involve dense regression and feature extraction. The project's meticulous, controlled experiment, comparing a depth-initialized model against one trained from scratch, provides strong evidence for this transfer effect, showcasing a consistent advantage across multiple test sets. This echoes findings in “worldproof: diagnosing where world-model predictions break and a measurement of when pixel metrics stop being able to rank models at all [P” regarding the limitations of current evaluation metrics and the potential for unexpected performance gains when leveraging pre-trained components, underscoring the need for deeper diagnostic tools.
The beauty of this project lies in its pragmatism and accessibility. Rather than pursuing radical architectural changes, the author skillfully adapted an existing, readily available model – YOLO26 – demonstrating that impactful innovation can arise from thoughtful repurposing rather than constant reinvention. The minimal changes required to transform the depth decoder into an RGB restoration head, coupled with the careful preservation of the backbone and neck weights, are a testament to the underlying architectural similarities between depth estimation and image deraining. The use of ClearView's established evaluation framework ensures comparability with existing deraining models, allowing for a clear assessment of YOLO26-RGB’s performance relative to established benchmarks. The focus on real-time performance, achieved through a relatively compact model (nano and small versions), further enhances its practical appeal, making it a viable option for applications where speed is paramount. This contrasts with larger models like Restormer, which, while achieving higher PSNR scores, face deployment challenges due to their memory footprint, as noted in the original article.
Beyond the specific results, this work contributes to a broader understanding of how pre-training can unlock latent capabilities within existing architectures. The observation that the depth-initialized model exhibits a performance advantage early in training, a gap that doesn’t close with further epochs, suggests that the depth training process imparts valuable spatial understanding. While the author rightly acknowledges that the exact mechanism remains unclear – whether it's geometry or simply a strong pre-trained checkpoint – the implications are significant. It implies that seemingly disparate tasks might share more architectural commonalities than previously assumed, and that pre-training on one task could provide a beneficial starting point for others. This aligns with ongoing research exploring the transferability of learned representations across different domains, as evidenced by discussions surrounding “worldproof: diagnosing where world-model predictions break and a measurement of when pixel metrics stop being able to rank models at all [P”, which highlights the challenges in accurately evaluating and comparing models trained on different datasets and tasks.
Ultimately, this project serves as a compelling case study for efficient model adaptation and the power of transfer learning. The ability to achieve competitive deraining performance with a minimal modification to an existing depth-estimation model, while maintaining real-time processing speeds, is a significant achievement. It prompts a crucial question: how can we systematically identify and leverage these hidden synergies between different computer vision tasks, moving beyond simply training models from scratch and instead exploring the potential of repurposing existing architectures to unlock new capabilities and efficiencies? The open-source nature of the code and models further democratizes access to this innovation, encouraging further exploration and experimentation within the community.
| | YOLO26 ships a depth-estimation model — dense, full-resolution, per-pixel regression, a task architecturally much closer to image restoration than to detection. I wanted to know whether the backbone+neck weights it learns through depth training transfer to a different dense-regression task (deraining), compared with training the same architecture from scratch. The deraining model that came out of it is a useful byproduct, but the transfer result is the part I think is worth discussing. What I inherited from YOLO26-depth
What I changed / added
How it was trained and measured ClearView as an external lib — its mixed synthetic+real rain recipe, Charbonnier loss, and 10-test-set protocol — so the numbers land on ClearView's own model-zoo scale, not a benchmark I made up. Released scales: nano (5.25M) and small (12.13M). Loading the YOLO26-depth checkpoint into this architecture matches 468/468 backbone+neck tensors exactly — only the new The transfer result (the interesting bit) A controlled initialization experiment at nano scale — same architecture, same recipe, fixed 100 epochs each — backbone+neck from the YOLO26-depth checkpoint vs. random init:
Deltas are from the unrounded averages (27.935 vs 27.452 PSNR). Small, but the depth init wins on every one of the 10 test sets. (These are 10-set averages, AllWeather included, from the 100-epoch controlled run — so they're lower and not directly comparable to the 9-rain-only released-model numbers in the ranking table below, which come from longer training.) On "did the random model just need longer to converge?" — both conditions ran a fixed 100 epochs, and the gap isn't a convergence-speed artifact: a 1-epoch check was a statistical wash, by 20 epochs the gap was already ~+0.49 dB, and at 100 epochs it was +0.48. It appeared early and didn't close with more training. This does not establish why — whether depth supervision teaches geometry/spatial structure that's useful for restoration, or whether YOLO26-depth just happens to be a strong pretrained checkpoint. Only that, in this setup, the depth-initialized representation is a better starting point than random. Per-dataset deltas are in the repo; happy to paste them in a comment. Accuracy (avg PSNR over 9 rain-only test sets, ClearView's ranking convention):
ClearView's own analysis points to the classification stem's early downsampling (a stride-4 entry before any residual block runs) as a likely reason the ResNet-UNet baselines underperform. This project doesn't test that directly — the ResNet-UNet comparison is a whole-architecture comparison, not a pretraining ablation — but it's the context the depth-vs-random experiment sits in. Note NAFNet-Small (1.1M, 31.15 dB): smaller and higher PSNR than yolo26_rgb_n, but ~4× slower (26.9 qps). So this isn't Pareto-dominant on every axis — the story is specifically the real-time / YOLO-derived operating point, not "more efficient in every sense." Deployment (TensorRT fp16, 1920×1080, batch 1, RTX 4070 SUPER 12GB; baseline figures are ClearView's own on the same GPU/TRT version) The clean pairwise comparisons against the ResNet-UNet family:
What this shows — and what it doesn't
Practical limitations: deraining is partial (faint streaks survive up close; dense rain over flat, low-texture backgrounds is the worst case). AllWeather (rain+fog) is out of domain — both YOLO26-RGB models and every ClearView baseline land around 13.5 dB, so it's excluded from the ranking. One task, two scales — not a general restoration model. Solo side project. AGPL-3.0 (inherited from Ultralytics' YOLO26 license). Not affiliated with Ultralytics.
Happy to answer questions on the architecture or the eval setup. [link] [comments] |
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