From 21c284751f224bac1a1f190a21f40e3f581401dd Mon Sep 17 00:00:00 2001
From: Haoqing Wang <12211045+haoqing-wang@user.noreply.gitee.com>
Date: Thu, 2 Mar 2023 03:32:34 +0000
Subject: [PATCH] add LocalMIM

Signed-off-by: Haoqing Wang <12211045+haoqing-wang@user.noreply.gitee.com>
---
 .../gpu/1.8/localmim_v1.0_imagenet2012.md     | 67 +++++++++++++++++++
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 create mode 100644 mshub_res/assets/noah-cvlab/gpu/1.8/localmim_v1.0_imagenet2012.md

diff --git a/mshub_res/assets/noah-cvlab/gpu/1.8/localmim_v1.0_imagenet2012.md b/mshub_res/assets/noah-cvlab/gpu/1.8/localmim_v1.0_imagenet2012.md
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+# LocalMIM
+
+---
+
+model-name: LocalMIM
+
+backbone-name: vit/swin_transformer
+
+module-type: cv-classification
+
+fine-tunable: True
+
+input-shape: [224, 224, 3]
+
+model-version: 1.0
+
+train-dataset: imagenet2012
+
+accuracy: vit-base: 84.02 / swin-base: 84.08
+
+author: Noah's Ark Lab
+
+update-time: 2023-3-2
+
+vit-repo-link: https://gitee.com/mindspore/models/tree/master/official/cv/VIT
+
+swin-repo-link: https://gitee.com/mindspore/models/tree/master/official/cv/SwinTransformer
+
+user-id: Noah's Ark Lab
+
+used-for: inference
+
+train-backend: gpu/ascend
+
+infer-backend: gpu/ascend
+
+mindspore-version: 1.8
+
+asset:
+
+- file-format: ckpt
+
+  asset-link: https://download.mindspore.cn/models/r1.8/localmim_vit_base_ascend_v180_imagenet2012_research_cv_top1acc84.02.ckpt
+
+  asset-link: https://download.mindspore.cn/models/r1.8/localmim_swin_base_ascend_v180_imagenet2012_research_cv_top1acc84.08.ckpt
+
+license: Apache2.0
+
+summary: Masked Image Modeling with Local Multi-Scale Reconstruction for imagenet classification.
+
+---
+
+## Introduction
+
+This MindSpore Hub model uses the implementation of vit and swin transformer from the MindSpore model zoo on above repo link.
+
+## Citation
+
+1. Haoqing Wang, Yehui Tang, Kai Han, Jianyuan Guo, Zhi-Hong Deng, Yunhe Wang. **Masked Image Modeling with Local Multi-Scale Reconstruction**. CVPR 2023.
+
+## Disclaimer
+
+MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes.
+
+To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated.
+
+MindSpore is available under the Apache 2.0 license, please see the LICENSE file.
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