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326 lines
16 KiB
326 lines
16 KiB
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# Welcome to the Model Zoo for MindSpore
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In order to facilitate developers to enjoy the benefits of MindSpore framework and Huawei chips, we will continue to add typical networks and models . If you have needs for the model zoo, you can file an issue on [gitee](https://gitee.com/mindspore/mindspore/issues) or [MindSpore](https://bbs.huaweicloud.com/forum/forum-1076-1.html), We will consider it in time.
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- SOTA models using the latest MindSpore APIs
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- The best benefits from MindSpore and Huawei chips
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- Officially maintained and supported
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# Table of Contents
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- [Models and Implementations](#models-and-implementations)
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- [Computer Vision](#computer-vision)
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- [Image Classification](#image-classification)
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- [GoogleNet](#googlenet)
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- [ResNet50[benchmark]](#resnet50)
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- [ResNet101](#resnet101)
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- [VGG16](#vgg16)
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- [AlexNet](#alexnet)
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- [LeNet](#lenet)
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- [Object Detection and Segmentation](#object-detection-and-segmentation)
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- [YoloV3](#yolov3)
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- [MobileNetV2](#mobilenetv2)
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- [MobileNetV3](#mobilenetv3)
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- [SSD](#ssd)
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- [Natural Language Processing](#natural-language-processing)
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- [BERT](#bert)
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- [MASS](#mass)
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- [Transformer](#transformer)
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# Announcements
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| Date | News |
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| ------------ | ------------------------------------------------------------ |
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| May 31, 2020 | Support [MindSpore v0.3.0-alpha](https://www.mindspore.cn/news/newschildren?id=215) |
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# Models and Implementations
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## Computer Vision
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### Image Classification
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#### [GoogleNet](#table-of-contents)
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| Parameters | GoogleNet |
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| -------------------------- | ------------------------------------------------------------ |
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| Published Year | 2014 |
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| Paper | [Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842) |
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| Resource | Ascend 910 |
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| Features | • Mixed Precision • Multi-GPU training support with Ascend |
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| MindSpore Version | 0.3.0-alpha |
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| Dataset | CIFAR-10 |
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| Training Parameters | epoch=125, batch_size = 128, lr=0.1 |
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| Optimizer | Momentum |
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| Loss Function | Softmax Cross Entropy |
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| Accuracy | 1pc: 93.4%; 8pcs: 92.17% |
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| Speed | 79 ms/Step |
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| Loss | 0.0016 |
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| Params (M) | 6.8 |
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| Checkpoint for Fine tuning | 43.07M (.ckpt file) |
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| Model for inference | 21.50M (.onnx file), 21.60M(.geir file) |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/googlenet |
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#### [ResNet50](#table-of-contents)
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| Parameters | ResNet50 |
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| -------------------------- | -------- |
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| Published Year | |
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#### [ResNet101](#table-of-contents)
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| Parameters | ResNet101 |
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| -------------------------- | --------- |
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| Published Year | |
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#### [VGG16](#table-of-contents)
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| Parameters | VGG16 |
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| -------------------------- | ----- |
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| Published Year | |
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#### [AlexNet](#table-of-contents)
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| Parameters | AlexNet |
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| -------------------------- | ------- |
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| Published Year | 2012 |
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| Paper | [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-) |
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| Resource | Ascend 910 |
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| Features | support with Ascend, GPU |
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| MindSpore Version | 0.5.0-beta |
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| Dataset | CIFAR10 |
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| Training Parameters | epoch=30, batch_size=32 |
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| Optimizer | Momentum |
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| Loss Function | SoftmaxCrossEntropyWithLogits |
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| Accuracy | 88.23% |
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| Speed | 1481fps |
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| Loss | 0.108 |
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| Params (M) | 61.10 |
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| Checkpoint for Fine tuning | 445MB(.ckpt file) |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/alexnet|
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#### [LeNet](#table-of-contents)
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| Parameters | LeNet |
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| -------------------------- | ----- |
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| Published Year | 1998 |
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| Paper | [Gradient-Based Learning Applied to Document Recognition](https://ieeexplore.ieee.org/abstract/document/726791) |
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| Resource | Ascend 910 |
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| Features | support with Ascend, GPU, CPU |
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| MindSpore Version | 0.5.0-beta |
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| Dataset | MNIST |
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| Training Parameters | epoch=10, batch_size=32 |
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| Optimizer | Momentum |
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| Loss Function | SoftmaxCrossEntropyWithLogits |
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| Accuracy | 98.52% |
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| Speed | 18680fps |
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| Loss | 0.004 |
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| Params (M) | 0.06 |
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| Checkpoint for Fine tuning | 483KB(.ckpt file) |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/lenet|
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### Object Detection and Segmentation
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#### [YoloV3](#table-of-contents)
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| Parameters | YoLoV3 |
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| -------------------------------- | ------ |
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| Mean Average Precision (mAP@0.5) | |
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#### [MobileNetV2](#table-of-contents)
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| Parameters | MobileNetV2 |
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| -------------------------------- | ----------- |
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#### [MobileNetV3](#table-of-contents)
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| Parameters | MobileNetV3 |
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| -------------------------------- | ----------- |
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#### [SSD](#table-of-contents)
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| Parameters | SSD |
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| -------------------------------- | ---- |
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## Natural Language Processing
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#### [BERT](#table-of-contents)
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| Parameters | BERT |
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| -------------------------- | ---- |
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| Published Year | |
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| GLUE Score | |
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#### [MASS](#table-of-contents)
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| Parameters | MASS |
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| -------------------------- | ---- |
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| ROUGE Score | |
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#### [Transformer](#table-of-contents)
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| Parameters | Transformer |
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| -------------------------- | -------------------------------------------------------------- |
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| Published Year | 2017 |
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| Paper | [Attention Is All You Need ](https://arxiv.org/abs/1706.03762) |
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| Resource | Ascend 910 |
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| Features | • Multi-GPU training support with Ascend |
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| MindSpore Version | 0.5.0-beta |
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| Dataset | WMT Englis-German |
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| Training Parameters | epoch=52, batch_size=96 |
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| Optimizer | Adam |
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| Loss Function | Softmax Cross Entropy |
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| BLEU Score | 28.7 |
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| Speed | 410ms/step (8pcs) |
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| Loss | 2.8 |
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| Params (M) | 213.7 |
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| Checkpoint for inference | 2.4G (.ckpt file) |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/Transformer |
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#### License
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[Apache License 2.0](https://github.com/mindspore-ai/mindspore/blob/master/LICENSE)
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