- 了解深度学习在视觉和自然语言处理中的应用
- 具备PyTorch和基于transformer模型的经验
- 熟悉多模态模型架构
目标受众
- 电脑视觉工程师
- AI开发者
Fine-Tuning 视觉语言模型（VLMs）是一种专业技能，用于增强多模态AI系统，这些系统处理视觉和文本输入，以应用于现实世界。
这项由讲师指导的培训（线上或线下）针对高级计算机视觉工程师和AI开发人员，他们希望微调如CLIP和Flamingo等VLMs，以提高在行业特定视觉文本任务中的表现。
培训结束后，参与者将能够：
- 理解视觉语言模型的架构和预训练方法。
- 微调VLMs以进行分类、检索、字幕生成或多模态问答。
- 准备数据集并应用PEFT策略以减少资源使用。
- 评估并在生产环境中部署定制的VLMs。
课程形式
- 互动式讲座和讨论。
- 大量练习和实践。
- 在实时实验室环境中进行动手实作。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
视觉语言模型简介
- VLMs概述及其在多模态AI中的角色
- 流行架构：CLIP、Flamingo、BLIP等
- 应用案例：搜索、字幕生成、自动化系统、内容分析
准备Fine-Tuning环境
- 设置OpenCLIP及其他VLM库
- 图像-文本对的数据集格式
- 视觉和语言输入的预处理管道
Fine-Tuning CLIP及类似模型
- 对比损失与联合嵌入空间
- 实操：在自定义数据集上微调CLIP
- 处理领域特定及多语言数据
高级Fine-Tuning技术
- 使用LoRA和基于适配器的方法提升效率
- 提示调优与视觉提示注入
- 零样本与微调评估的权衡
评估与基准测试
- VLMs的评估指标：检索准确率、BLEU、CIDEr、召回率
- 视觉-文本对齐诊断
- 可视化嵌入空间与错误分类
部署与实际应用
- 导出模型以进行推理（TorchScript、ONNX）
- 将VLMs集成到管道或API中
- 资源考虑与模型扩展
案例研究与应用场景
- 媒体分析与内容审核
- 电子商务与数字图书馆中的搜索与检索
- 机器人与自动化系统中的多模态交互
总结与下一步
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