salesforce/BLIP
BLIP
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.
Features
- Training Frameworks - Provides an open-source framework for training, fine-tuning, and evaluating vision-language models on custom image-text datasets.
- Encoder-Decoder Architectures - Processes images and text through separate encoders then fuses them in a shared transformer decoder for generation tasks.
- Multimodal Contrastive Losses - Combines three training objectives to align image-text pairs, classify matching, and generate fluent captions.
- Data-Parallel Training - Replicates the model across multiple GPUs and synchronizes gradients and metrics using all-reduce communication.
- Text-to-Image Retrieval - Finds images whose content matches a text query using learned embeddings.
- Image-Text Match Ranking - Scores how well an image and a piece of text correspond to each other, returning a compatibility score.
- Image-to-Text Retrieval - Finds the most relevant captions or descriptions from a collection given an image query.
- Vision-Language Training - Provides specialized training workflows for models that process both visual and textual data.
- Cross-Modal Similarity Scoring - Computes cosine similarity between image and text embeddings to rank and retrieve matching pairs from a collection.
- Visual Question Answering - Trains vision-language models to answer natural language questions about visual content.
- Image Captioning - Generates descriptive natural-language text summarizing the visual content of an input image using a vision-language model.
- Visual Question Answering Libraries - Generates natural-language descriptions and answers visual questions from images using multimodal encoder-decoder architectures.
- Multimodal Feature Extractors - Produces a unified vector representation from an image, text, or both for downstream tasks.
- Stepwise Decay Schedules - Reduces the learning rate by a constant factor each epoch while clamping it above a configured minimum value.
- Cosine Warmup Schedules - Ramps the learning rate linearly then decays it along a cosine curve to stabilize early training and converge smoothly.
- From-Scratch Trainings - Trains multimodal models from scratch on large collections of image-caption pairs.
- Vision-Language Fine-Tunings - Adapts pretrained vision-language models to custom tasks using distributed training on custom data.
- Caption-Based Training - Trains vision-language models to generate descriptive captions for images using paired image-caption datasets.
- Visual Question Answering Evaluation - Runs trained models on test datasets to generate or rank answers for image-question pairs and collects results for scoring.
- Cross-Modal Retrieval Training - Trains models to align images and text by minimizing contrastive and matching losses for cross-modal retrieval.
- Cross-Modal Retrieval Frameworks - Scores image-text alignment and retrieves matching images or captions using learned multimodal embeddings.
- Vision Language Models - Unified encoder-decoder architecture for image-language pre-training.
Star history
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