MIC-DKFZ / ResEncL-OpenMind-SimCLR

huggingface.co
Total runs: 22
24-hour runs: -1
7-day runs: -1
30-day runs: 11
Model's Last Updated: May 07 2025
image-feature-extraction

Introduction of ResEncL-OpenMind-SimCLR

Model Details of ResEncL-OpenMind-SimCLR

OpenMind Benchmark 3D SSL Models

Model from the paper : An OpenMind for 3D medical vision self-supervised learning
Pre-training codebase used to create checkpoint : MIC-DKFZ/nnssl
Dataset : AnonRes/OpenMind
Downstream (segmentation) fine-tuning : TaWald/nnUNet


OpenMind

Overview

This repository hosts pre-trained checkpoints from the OpenMind benchmark:
📄 An OpenMind for 3D medical vision self-supervised learning (Wald, T., Ulrich, C., Suprijadi, J., Ziegler, S., Nohel, M., Peretzke, R., ... & Maier-Hein, K. H. (2024).)
( arXiv:2412.17041 ) — the first extensive benchmark study for self-supervised learning (SSL) on 3D medical imaging data.

Each model was pre-trained using a particular SSL method on the OpenMind Dataset , a large-scale, standardized collection of public brain MRI datasets.

These models are not recommended to be used as-is for feature extraction. Instead we recommend using the downstream fine-tuning frameworks for segmentation and classification adaptation, available in the adaptation repository . While manual download is possible, we recommend using the auto-download feature of the fine-tuning repository by providing the repository URL on Hugging Face instead of a local checkpoint path.


Model Variants

We release SSL checkpoints for two backbone architectures:

Each encoder has been pre-trained using one of the following SSL techniques:

Method Description
Volume Contrastive (VoCo) Contrastive pretraining method for 3D volumes
VolumeFusion (VF) Spatial volume fusion-based segmentation SSL method
Models Genesis (MG) Reconstruction and denoising based pretraining method
Masked Autoencoders (MAE) Default reconstruction based pretraining method
Spark 3D (S3D) Sparse reconstruction based pretraining mehtod (CNN only)
SimMIM Simple masked reconstruction based pretraining method (TR only)
SwinUNETR SSL Rotation, Contrastive and Reconstruction based pre-training method.
SimCLR Transfer of 2D Contrastive learning baseline method to 3D

Runs of MIC-DKFZ ResEncL-OpenMind-SimCLR on huggingface.co

22
Total runs
-1
24-hour runs
-2
3-day runs
-1
7-day runs
11
30-day runs

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ResEncL-OpenMind-SimCLR huggingface.co

ResEncL-OpenMind-SimCLR huggingface.co is an AI model on huggingface.co that provides ResEncL-OpenMind-SimCLR's model effect (), which can be used instantly with this MIC-DKFZ ResEncL-OpenMind-SimCLR model. huggingface.co supports a free trial of the ResEncL-OpenMind-SimCLR model, and also provides paid use of the ResEncL-OpenMind-SimCLR. Support call ResEncL-OpenMind-SimCLR model through api, including Node.js, Python, http.

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https://huggingface.co/MIC-DKFZ/ResEncL-OpenMind-SimCLR

ResEncL-OpenMind-SimCLR install

ResEncL-OpenMind-SimCLR is an open source model from GitHub that offers a free installation service, and any user can find ResEncL-OpenMind-SimCLR on GitHub to install. At the same time, huggingface.co provides the effect of ResEncL-OpenMind-SimCLR install, users can directly use ResEncL-OpenMind-SimCLR installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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