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一筐大模型揉一起做篮球识别！ 给大家看这个识别效果，投篮位置，是否进球，球衣编号，球在哪里(甚至裁判手里拿的另外一个球都能捕捉到)，篮筐，运动员都能识别。 总计用了这些模型： F-DETR （检测球员）—— 这是个类 DETR 的实时目标检测器。微调后用来检测球员、球衣号码、裁判、篮球，甚至投篮类型。 SAM2 （跟踪球员）—— 用于分割与跟踪。它在球员被遮挡后重新识别，并在身体接触中保持目标 ID 稳定。 SigLIP + UMAP + K-means （无监督球队聚类）— 结合视觉-语言嵌入与无监督聚类，通过统一的颜色和纹理将球员自动分组，无需人工标注 SmolVLM2 （识别球员号码）— 这个比较猛，今年2月份发布的，有256M, 500M, 2.2B 三个版本。一般用在OCR场景，是个VLM，经过 NBA 球衣裁剪图像微调后，识别队服和编号准确率从 56%提升至 86% ResNet-32 — （号码分类）一种经典 CNN，经过微调用于球衣号码分类，测试准确率达到 93%，优于微调后的 SmolVLM2 原文写的非常棒，作为学习资料足够了，推荐给大家：
A coder, road bike rider, server fortune teller, electronic waste collector, co-founder of KCORES, ex-director at IllaSoft, KingsoftOffice, Juejin.
now I really want a good ai first email client with strong CRM integration (that’s not a sales tool)
VC by day @untappedvc, builder by night: @babyagi_, @pippinlovesyou @pixelbeastsnft. Build-in-public log: https://t.co/UdHHGbZba5
6 dads vs. 1 hotel provided stroller
Marketer, self-taught developer, and founder of @Bento and https://t.co/lcsIohchEv. Designing a quiet family life in 福岡, Japan. DMs open if you need email help 🌿
RT @jonsommet: @shl You have to choose safe or affordable.
Founder/CEO @Gumroad
RT @togethercompute: 📄New Guide: Running nanochat on instant clusters! Train and inference @karpathy's end-to-end ChatGPT clone on Toget…
Building @EurekaLabsAI. Previously Director of AI @ Tesla, founding team @ OpenAI, CS231n/PhD @ Stanford. I like to train large deep neural nets.
RT @arvidkahl: Either this is a really great security process or a very devious way of getting people to sign up for an affiliate program.…
Building https://t.co/od97B0HVrk and https://t.co/666FnyVVE0 in Public. Raising all the boats with kindness. 🎙️ https://t.co/6w69DZmi8H · ✍️ https://t.co/lpnor5rsTW