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Tic CLIP Bestpool Cumulative

Developed by apple
TiC-CLIP is an improved vision-language model based on OpenCLIP, employing continual training strategies on time-series data to effectively reduce computational costs for model updates.
Downloads 313
Release Time : 6/5/2024

Model Overview

This model serves as a benchmark suite for continual training of vision-language models, containing timestamped image-text pair data spanning 9 years (2014-2022), supporting zero-shot image classification and cross-modal retrieval tasks.

Model Features

Temporal Continual Training
Adopts continual training strategy to avoid complete retraining, reducing computation by 2.5x compared to standard methods
Large-scale Time-series Data
Based on TiC-DataComp dataset containing 12.7 billion timestamped image-text pairs from 2014-2022
Efficient Replay Strategy
Maintains model performance by continuing training from last checkpoint and replaying old data

Model Capabilities

Zero-shot image classification
Image-text matching
Cross-modal retrieval
Continual learning

Use Cases

Computer Vision Research
Continual Learning Method Development
Researchers can use this model to accelerate development of continual learning methods
Starting from pre-trained checkpoints for continual training on subsequent yearly/monthly data
Cross-modal Applications
Image Retrieval Systems
Building time-series based image retrieval systems
Achieves 8% higher accuracy than traditional CLIP models on 2021-2022 retrieval tasks
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