BLIP is a vision-language model framework that combines contrastive, matching, and language modeling objectives to align images with text. Built on a multimodal encoder-decoder architecture, it supports distributed data-parallel training with cosine learning rate scheduling and sliding-window metric tracking for training stability.
The framework provides capabilities for image captioning, visual question answering, and cross-modal retrieval, scoring semantic alignment between images and text through learned embeddings. It includes toolkits for fine-tuning pre-trained models on custom datasets and training vision-language models from scratch, with support for evaluating caption quality, visual reasoning accuracy, and video-text retrieval performance.
Training workflows incorporate learning rate scheduling with warmup, stepwise decay, and cosine decay, while distributed training metrics are synchronized across GPU workers via all-reduce communication. The system also supports extracting unified multimodal features for downstream tasks and logging training progress with periodic summaries.
