https://github.com/mindspore-courses/deep-tutorials-for-mindspore
https://github.com/mindspore-courses/deep-tutorials-for-mindspore
mindspore tutorial
Last synced: 25 days ago
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- Host: GitHub
- URL: https://github.com/mindspore-courses/deep-tutorials-for-mindspore
- Owner: mindspore-courses
- Created: 2023-07-18T14:03:39.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-07T01:39:21.000Z (over 1 year ago)
- Last Synced: 2025-04-28T14:21:39.864Z (25 days ago)
- Topics: mindspore, tutorial
- Language: Python
- Homepage:
- Size: 98.6 KB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Deep-Tutorials-for-MindSpore
The code of this repository is referenced to [Deep-Tutorial-for-PyTorch](https://github.com/sgrvinod/Deep-Tutorials-for-PyTorch)
---
These tutorials is the implementation of some typical papers. Below is the code directories and their corresponding papers.
Tutorial | Paper
:---: | :---:
Image Captioning | [_Show, Attend, and Tell_](https://arxiv.org/abs/1502.03044)
Sequence Labeling | [_Empower Sequence Labeling with Task-Aware Neural Language Model_](https://arxiv.org/abs/1709.04109)
Object Detection | [_SSD: Single Shot MultiBox Detector_](https://arxiv.org/abs/1512.02325)
Text Classification | [_Hierarchical Attention Networks for Document Classification_](https://www.semanticscholar.org/paper/Hierarchical-Attention-Networks-for-Document-Yang-Yang/1967ad3ac8a598adc6929e9e6b9682734f789427)
Super-Resolution | [_Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network_](https://arxiv.org/abs/1609.04802)
Machine Translation | [_Attention Is All You Need_](https://arxiv.org/abs/1706.03762)
---
Take ImageCaptioning as an example to introduce the file dictionary structure, the others are similar.
```
.
|--ImageCaptioning
| |--create_input_files.py // Process source data files
| |--utils.py // Utility module
| |--datasets.py // Create data source for GeneratorDataset
| |--models.py // Model file
| |--train.py // Train the model
| |--eval.py // Evaluate the model
| |--caption.py // Caption the input image
```