Kled V3 发布：72 小时内向 50 万+ 贡献者网络派发数据采集任务
You can't scrape a dataset that doesn't exist yet. Think about training a model on people performing a specific task, in a specific environment, from a specific camera angle. Someone has to go and capture those examples. This is what makes Kled V3 interesting. Labs can specify the data they need, and Kled can deploy collection tasks to its network of 500,000+ opt-in contributors within 72 hours. 108 configurable templates across image, video, audio, text, and annotation. Contributors capture the data on their phones, with instructions and examples of what qualifies. The opportunity here is a much tighter feedback loop: Identify where a model fails. Turn that failure into a collection task. Get new examples from the real world. Train and evaluate again. The ability to repeatedly collect the exact data a model is missing could be very powerful.
Kled V3 允许实验室指定所需数据，并在 72 小时内将采集任务部署到其 50 万+ 自愿贡献者网络，覆盖图像、视频、音频、文本和标注的 108 个可配置模板。贡献者用手机按说明和合格示例完成采集，形成"定位模型失败点—转为采集任务—获取真实世界新样本—再训练评估"的更紧反馈闭环。
来源：X：Elvis Saravia (@omarsar0, DAIR.AI) · x.lingyaoai.com