ALIGN-base
| Property | Value |
|---|---|
| Author | kakaobrain |
| Paper | arXiv:2102.05918 |
| Training Data | COYO-700M dataset |
| Architecture | EfficientNet (vision) + BERT (text) |
What is align-base?
ALIGN-base is a powerful multi-modal model that combines vision and language understanding using a dual-encoder architecture. Developed by Kakao Brain, it's trained on the COYO-700M dataset and achieves performance comparable to Google's original ALIGN model despite using a smaller training dataset.
Implementation Details
The model implements a dual-encoder architecture utilizing EfficientNet for visual processing and BERT for text processing. It learns to align visual and text representations through contrastive learning, enabling zero-shot image classification and multi-modal embedding retrieval.
- Trained on COYO-700M dataset with 700 million image-text pairs
- Implements contrastive learning for vision-language alignment
- Supports zero-shot classification and embedding generation
Core Capabilities
- Zero-shot image classification
- Multi-modal embedding retrieval
- Separate image and text embedding generation
- Cross-modal similarity scoring
Frequently Asked Questions
Q: What makes this model unique?
ALIGN-base stands out for achieving comparable performance to Google's ALIGN model while using a significantly smaller, open-source dataset (COYO-700M vs 1.8B pairs). It demonstrates that careful curation of training data can compensate for dataset size.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, particularly in exploring zero-shot image classification and vision-language understanding. It's well-suited for tasks like image-text similarity scoring, embedding generation, and multi-modal retrieval applications.
