Prerequisites
- Transformer architecture fundamentals
- Multimodal models (CLIP, BLIP, LayoutLMv2)
- Traditional OCR methods (Tesseract, EasyOCR, PaddleOCR)
- PyTorch/HuggingFace skills
Quick Start
- Environment setup
- Model loading
- Output parsing (text/coordinates/tags)
- Parameter experiments (base_size, crop_mode, Prompt)
- Documentation reading and code walkthrough
Training and Fine-tuning
- Data preparation
- Understanding original training strategy
- Choosing training approach (freeze encoder/LoRA)
- Hyperparameter settings
- Evaluation
Deployment Options
- Web applications
- Office system integration
- AI assistant tools
- Edge/private deployment
- Secondary development
Error Troubleshooting
- Installation failures
- Slow inference
- CUDA OOM
- Coordinate alignment errors
- Chinese garbled text
- Weight download failures
- Table/layout issues
- Fine-tuning problems
Learning Strategy
“Run first, then customize” strategy. Recommend incremental fine-tuning approach rather than full retraining.