| --- |
| license: apache-2.0 |
| tags: |
| - self-supervised learning |
| - vision |
| - GMML |
| inference: false |
|
|
| --- |
| |
| # Model description |
|
|
| GMML is a self-supervised learning model that learns to group masked pixels in an image. The model is trained on ImageNet-1K. |
|
|
|
|
| # Model Sources |
|
|
| - https://github.com/Sara-Ahmed/GMML |
| - https://arxiv.org/abs/2205.14986 |
|
|
|
|
| # Model Card Authors |
| Sara Atito, Muhammad Awais, Josef Kittler |
|
|
| # How to use |
|
|
| ```python |
| from transformers import BertConfig, BertModel |
| |
| config = BertConfig() |
| model = BertModel(config) |
| |
| model.push_to_hub("nielsr/my-awesome-bert-model") |
| |
| # reload |
| model = BertModel.from_pretrained("nielsr/my-awesome-bert-model") |
| ``` |
|
|
| # BibTeX entry and citation info |
| ``` |
| @inproceedings{atito2023gmml, |
| title={GMML is all you need}, |
| author={Atito, Sara and Awais, Muhammed and Nandam, Srinivasa and Kittler, Josef}, |
| booktitle={2023 IEEE International Conference on Image Processing (ICIP)}, |
| pages={2125--2129}, |
| year={2023}, |
| organization={IEEE} |
| } |
| ``` |