license: other
license_name: custom-apple-license
license_link: https://github.com/apple/ml-tic-clip/blob/main/LICENSE
tags:
- vision
- zero-shot-image-classification
datasets:
- apple/TiC-DataComp
Model Card for Model ID
This repository contains TiC-CLIP models trained on TiC-DataComp-Yearly with data from 2014 to 2022 using our modified OpenCLIP code. For additional information refer to our GitHub repo.
Model Details
Model Description
Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models. This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines. We introduce the first set of web-scale Time-Continual (TiC) benchmarks for training vision-language models: TiC-DataComp, TiC-YFCC, and TiC-Redcaps. TiC-DataComp, our largest dataset, contains over 12.7B timestamped image-text pairs spanning 9 years (2014-2022). We first use our benchmarks to curate various dynamic evaluations to measure temporal robustness of existing models. We show OpenAI's CLIP (trained on data up to 2020) loses ≈8% zero-shot accuracy on our curated retrieval task from 2021-2022 compared with more recently trained models in OpenCLIP repository. We then study how to efficiently train models on time-continuous data. We demonstrate that a simple rehearsal-based approach that continues training from the last checkpoint and replays old data reduces compute by 2.5× when compared to the standard practice of retraining from scratch. Code is available at this https URL.
- Developed by: Apple
- License: See LICENSE
Model Sources [optional]
- Repository: ml-tic-clip GitHub repo
- Paper: TiC-CLIP: Continual Training of CLIP Models, Garg, S., Farajtabar, M., Pouransari, H., Vemulapalli, R., Mehta, S., Tuzel, O., Shankar, V. and Faghri, F., International Conference on Learning Representations (ICLR), 2024.
Uses
Researchers can use TiC-CLIP pretrained models for faster design of continual learning methods by start from a pretrained checkpoint and continually train on the next year or next month data.
How to Get Started with the Model
The models are compatible with DataComp evaluation suite and our patched version of DataComp for evaluation on TiC-DataComp-Retrieval and TiC-DataCompNet. The models can also be used to resume a training or as initialization for new training using OpenCLIP code. Please follow instructions in our GitHub repo to create the evaluation sets or follow DataComp for the standard evaluations on 38 datasets.
Training Details
Training Data
[More Information Needed]
Training Procedure
Please refer to Sections 2-3 of our TiC-CLIP paper.
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
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Metrics
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Results
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Summary
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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