📄 论文总结
BioBench：超越ImageNet的科学机器学习基准蓝图 / BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks
1️⃣ 一句话总结
这篇论文提出了一个名为BioBench的新基准测试，专门用于评估生态学领域的计算机视觉模型，解决了传统ImageNet基准在科学图像任务上表现不佳的问题，为构建可靠的AI科学基准提供了模板。
🤖 系统
Abstract - BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks
ImageNet-1K linear-probe transfer accuracy remains the default proxy for visual representation quality, yet it no longer predicts performance on scientific imagery. Across 46 modern vision model checkpoints, ImageNet top-1 accuracy explains only 34% of variance on ecology tasks and mis-ranks 30% of models above 75% accuracy. We present BioBench, an open ecology vision benchmark that captures what ImageNet misses. BioBench unifies 9 publicly released, application-driven tasks, 4 taxonomic kingdoms, and 6 acquisition modalities (drone RGB, web video, micrographs, in-situ and specimen photos, camera-trap frames), totaling 3.1M images. A single Python API downloads data, fits lightweight classifiers to frozen backbones, and reports class-balanced macro-F1 (plus domain metrics for FishNet and FungiCLEF); ViT-L models evaluate in 6 hours on an A6000 GPU. BioBench provides new signal for computer vision in ecology and a template recipe for building reliable AI-for-science benchmarks in any domain. Code and predictions are available at this https URL and results at this https URL.