Fine-Tuning Vision-Language Models (VLMs) Training Course
Fine-Tuning Vision-Language Models (VLMs) is a specialized skill used to enhance multimodal AI systems that process both visual and textual inputs for real-world applications in various sectors, including those relevant to government operations.
This instructor-led, live training (online or onsite) is aimed at advanced-level computer vision engineers and AI developers who wish to fine-tune VLMs such as CLIP and Flamingo to improve performance on industry-specific visual-text tasks for government use.
By the end of this training, participants will be able to:
- Understand the architecture and pretraining methods of vision-language models for government applications.
- Fine-tune VLMs for classification, retrieval, captioning, or multimodal QA tasks specific to public sector needs.
- Prepare datasets and apply Parameter-Efficient Fine-Tuning (PEFT) strategies to optimize resource usage in government environments.
- Evaluate and deploy customized VLMs in production environments tailored for government operations.
Format of the Course
- Interactive lecture and discussion focused on government applications.
- Lots of exercises and practice relevant to public sector workflows.
- Hands-on implementation in a live-lab environment designed for government use.
Course Customization Options
- To request a customized training for this course, specifically tailored for government needs, please contact us to arrange.
Course Outline
Introduction to Vision-Language Models for Government
- Overview of VLMs and their role in multimodal AI for government applications
- Popular architectures: CLIP, Flamingo, BLIP, etc., tailored for government use cases
- Use cases: search, captioning, autonomous systems, content analysis within public sector workflows
Preparing the Fine-Tuning Environment for Government
- Setting up OpenCLIP and other VLM libraries to support government operations
- Dataset formats for image-text pairs suitable for government datasets
- Preprocessing pipelines for vision and language inputs aligned with public sector standards
Fine-Tuning CLIP and Similar Models for Government
- Contrastive loss and joint embedding spaces optimized for government data
- Hands-on: fine-tuning CLIP on custom datasets relevant to government agencies
- Handling domain-specific and multilingual data in a government context
Advanced Fine-Tuning Techniques for Government
- Using LoRA and adapter-based methods for efficient model updates in government systems
- Prompt tuning and visual prompt injection to enhance government applications
- Zero-shot vs. fine-tuned evaluation trade-offs in government use cases
Evaluation and Benchmarking for Government
- Metrics for VLMs: retrieval accuracy, BLEU, CIDEr, recall, tailored for government performance standards
- Visual-text alignment diagnostics to ensure data integrity in government operations
- Visualizing embedding spaces and misclassifications to improve model reliability for government
Deployment and Use in Real Applications for Government
- Exporting models for inference (TorchScript, ONNX) to support government systems
- Integrating VLMs into pipelines or APIs for seamless government workflows
- Resource considerations and model scaling to meet government operational needs
Case Studies and Applied Scenarios for Government
- Media analysis and content moderation in government communications
- Search and retrieval in e-commerce and digital libraries managed by government entities
- Multimodal interaction in robotics and autonomous systems deployed by government agencies
Summary and Next Steps for Government
Requirements
- An understanding of deep learning for vision and natural language processing (NLP)
- Experience with PyTorch and transformer-based models
- Familiarity with multimodal model architectures
Audience
- Computer vision engineers for government
- AI developers for government
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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