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![](https://www.mindspore.cn/static/img/logo.a3e472c9.png)
# Welcome to the Model Zoo for MindSpore
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.
- SOTA models using the latest MindSpore APIs
- The best benefits from MindSpore and Huawei chips
- Officially maintained and supported
# Table of Contents
- [Models and Implementations](#models-and-implementations)
- [Computer Vision](#computer-vision)
- [Image Classification](#image-classification)
- [GoogleNet](#googlenet)
- [ResNet50[benchmark]](#resnet50)
- [ResNet101](#resnet101)
- [VGG16](#vgg16)
- [AlexNet](#alexnet)
- [LeNet](#lenet)
- [Object Detection and Segmentation](#object-detection-and-segmentation)
- [YoloV3](#yolov3)
- [MobileNetV2](#mobilenetv2)
- [MobileNetV3](#mobilenetv3)
- [SSD](#ssd)
- [Natural Language Processing](#natural-language-processing)
- [BERT](#bert)
- [MASS](#mass)
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- [Transformer](#transformer)
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# Announcements
| Date | News |
| ------------ | ------------------------------------------------------------ |
| May 31, 2020 | Support [MindSpore v0.3.0-alpha](https://www.mindspore.cn/news/newschildren?id=215) |
# Models and Implementations
## Computer Vision
### Image Classification
#### [GoogleNet](#table-of-contents)
| Parameters | GoogleNet |
| -------------------------- | ------------------------------------------------------------ |
| Published Year | 2014 |
| Paper | [Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842) |
| Resource | Ascend 910 |
| Features | • Mixed Precision • Multi-GPU training support with Ascend |
| MindSpore Version | 0.3.0-alpha |
| Dataset | CIFAR-10 |
| Training Parameters | epoch=125, batch_size = 128, lr=0.1 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| Accuracy | 1pc: 93.4%; 8pcs: 92.17% |
| Speed | 79 ms/Step |
| Loss | 0.0016 |
| Params (M) | 6.8 |
| Checkpoint for Fine tuning | 43.07M (.ckpt file) |
| Model for inference | 21.50M (.onnx file), 21.60M(.geir file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/googlenet |
#### [ResNet50](#table-of-contents)
| Parameters | ResNet50 |
| -------------------------- | -------- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
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| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [ResNet101](#table-of-contents)
| Parameters | ResNet101 |
| -------------------------- | --------- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| Accuracy | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [VGG16](#table-of-contents)
| Parameters | VGG16 |
| -------------------------- | ----- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| Accuracy | |
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| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [AlexNet](#table-of-contents)
| Parameters | AlexNet |
| -------------------------- | ------- |
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| Published Year | 2012 |
| Paper | [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-) |
| Resource | Ascend 910 |
| Features | support with Ascend, GPU |
| MindSpore Version | 0.5.0-beta |
| Dataset | CIFAR10 |
| Training Parameters | epoch=30, batch_size=32 |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropyWithLogits |
| Accuracy | 88.23% |
| Speed | 1481fps |
| Loss | 0.108 |
| Params (M) | 61.10 |
| Checkpoint for Fine tuning | 445MB(.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/alexnet|
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#### [LeNet](#table-of-contents)
| Parameters | LeNet |
| -------------------------- | ----- |
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| Published Year | 1998 |
| Paper | [Gradient-Based Learning Applied to Document Recognition](https://ieeexplore.ieee.org/abstract/document/726791) |
| Resource | Ascend 910 |
| Features | support with Ascend, GPU, CPU |
| MindSpore Version | 0.5.0-beta |
| Dataset | MNIST |
| Training Parameters | epoch=10, batch_size=32 |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropyWithLogits |
| Accuracy | 98.52% |
| Speed | 18680fps |
| Loss | 0.004 |
| Params (M) | 0.06 |
| Checkpoint for Fine tuning | 483KB(.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/lenet|
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### Object Detection and Segmentation
#### [YoloV3](#table-of-contents)
| Parameters | YoLoV3 |
| -------------------------------- | ------ |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| Mean Average Precision (mAP@0.5) | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [MobileNetV2](#table-of-contents)
| Parameters | MobileNetV2 |
| -------------------------------- | ----------- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| Mean Average Precision (mAP@0.5) | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [MobileNetV3](#table-of-contents)
| Parameters | MobileNetV3 |
| -------------------------------- | ----------- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| Mean Average Precision (mAP@0.5) | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [SSD](#table-of-contents)
| Parameters | SSD |
| -------------------------------- | ---- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| Mean Average Precision (mAP@0.5) | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
## Natural Language Processing
#### [BERT](#table-of-contents)
| Parameters | BERT |
| -------------------------- | ---- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| GLUE Score | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
#### [MASS](#table-of-contents)
| Parameters | MASS |
| -------------------------- | ---- |
| Published Year | |
| Paper | |
| Resource | |
| Features | |
| MindSpore Version | |
| Dataset | |
| Training Parameters | |
| Optimizer | |
| Loss Function | |
| ROUGE Score | |
| Speed | |
| Loss | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
| Scripts | |
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#### [Transformer](#table-of-contents)
| Parameters | Transformer |
| -------------------------- | -------------------------------------------------------------- |
| Published Year | 2017 |
| Paper | [Attention Is All You Need ](https://arxiv.org/abs/1706.03762) |
| Resource | Ascend 910 |
| Features | • Multi-GPU training support with Ascend |
| MindSpore Version | 0.5.0-beta |
| Dataset | WMT Englis-German |
| Training Parameters | epoch=52, batch_size=96 |
| Optimizer | Adam |
| Loss Function | Softmax Cross Entropy |
| BLEU Score | 28.7 |
| Speed | 410ms/step (8pcs) |
| Loss | 2.8 |
| Params (M) | 213.7 |
| Checkpoint for inference | 2.4G (.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/Transformer |
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#### License
[Apache License 2.0](https://github.com/mindspore-ai/mindspore/blob/master/LICENSE)