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README.md

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+---
+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
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+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](https://github.com/apple/ml-tic-clip).
+
+## 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](https://github.com/apple/ml-tic-clip).
+
+
+
+- **Developed by:** Apple
+- **License:** See [LICENSE](https://github.com/apple/ml-tic-clip/blob/main/LICENSE)
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [ml-tic-clip GitHub repo](https://github.com/apple/ml-tic-clip)
+- **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.](https://arxiv.org/abs/2310.16226)
+
+## 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](https://github.com/apple/ml-tic-clip) to create the evaluation sets or follow [DataComp](https://github.com/mlfoundations/datacomp) for the standard evaluations on 38 datasets.
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+Please refer to Sections 2-3 of our [TiC-CLIP](https://github.com/apple/ml-tic-clip) paper.
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]