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CABiNet (2021) outperforms YOLO26-sem on UAVid dataset

By PulseAugur Editorial · [1 sources] ·

A comparison between the CABiNet (ICRA 2021) and YOLO26-sem (2026) models on the UAVid dataset reveals that CABiNet achieves higher accuracy with significantly less computational resources. The original author of CABiNet re-evaluated their model against YOLO26-sem variants, standardizing the dataset, class weighting, and evaluation protocol. Results show CABiNet's MobileNetV3-L variant reached 67.14% mIoU with 9.17M parameters and 54.8 GFLOPs, outperforming all YOLO26-sem variants in accuracy and efficiency. AI

IMPACT Demonstrates that older, efficient architectures can still outperform newer, larger models on specific tasks and datasets.

RANK_REASON Comparison of two computer vision models on a specific dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/MachineLearning →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CABiNet (2021) outperforms YOLO26-sem on UAVid dataset

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Comparison of two computer vision models on a specific dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Naive-Explanation940 ·

    CABiNet (ICRA 2021) vs YOLO26-sem on UAVid: accuracy, compute, and GPU latency [P]

    <table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1w5cfv1/cabinet_icra_2021_vs_yolo26sem_on_uavid_accuracy/"> <img alt="CABiNet (ICRA 2021) vs YOLO26-sem on UAVid: accuracy, compute, and GPU latency [P]" src="https://external-preview.redd.it/FQ3T6ncHYexw…

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