forked from huawei/mindspore2022
!7912 update ModelZoo README and googlenet README
From: @liyanliu96 Reviewed-by: @oacjiewen,@yingjy,@oacjiewen Signed-off-by: @yingjy
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@ -27,15 +27,15 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
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- [AlexNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet/README.md)
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- [LeNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/README.md)
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- [LeNet_Quant](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet_quant/README.md)
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- [InceptionV3](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3/README.md)
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- [MobileNetV2](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2/README.md)
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- [MobileNetV2_Quant](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2_quant/README.md)
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- [MobileNetV3](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv3/README.md)
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- [InceptionV3](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3/README.md)
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- [Object Detection and Segmentation](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv)
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- [DeepLabV3](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3/README.md)
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- [FasterRCNN](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/faster_rcnn/README.md)
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- [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53/README.md)
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- [YoloV3-ResNet18](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18/README.md)
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- [MobileNetV2](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2/README.md)
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- [MobileNetV2_Quant](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2_quant/README.md)
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- [MobileNetV3](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv3/README.md)
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- [MaskRCNN](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/maskrcnn/README.md)
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- [SSD](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd/README.md)
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- [Warp-CTC](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/warpctc/README.md)
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@ -59,6 +59,7 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
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# Announcements
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| Date | News |
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| ------------ | ------------------------------------------------------------ |
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| September 25, 2020 | Support [MindSpore v1.0.0](https://www.mindspore.cn/news/newschildren/en?id=262) |
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| September 01, 2020 | Support [MindSpore v0.7.0-beta](https://www.mindspore.cn/news/newschildren/en?id=246) |
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| July 31, 2020 | Support [MindSpore v0.6.0-beta](https://www.mindspore.cn/news/newschildren/en?id=237) |
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@ -36,7 +36,7 @@ GoogleNet, a 22 layers deep network, was proposed in 2014 and won the first plac
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# [Model Architecture](#contents)
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Specifically, the GoogleNet contains numerous inception modules, which are connected together to go deeper. In general, an inception module with dimensionality reduction consists of **1×1 conv**, **3×3 conv**, **5×5 conv**, and **3×3 max pooling**, which are done altogether for the previous input, and stack together again at output.
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Specifically, the GoogleNet contains numerous inception modules, which are connected together to go deeper. In general, an inception module with dimensionality reduction consists of **1×1 conv**, **3×3 conv**, **5×5 conv**, and **3×3 max pooling**, which are done altogether for the previous input, and stack together again at output. In our model architecture, the kernel size used in inception module is 3×3 instead of 5×5.
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@ -297,13 +297,14 @@ Parameters for both training and evaluation can be set in config.py
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## [Performance](#contents)
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### Evaluation Performance
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#### GoogleNet on CIFAR-10
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| Parameters | Ascend | GPU |
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| -------------------------- | ----------------------------------------------------------- | ---------------------- |
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| Model Version | Inception V1 | Inception V1 |
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| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G | NV SMX2 V100-32G |
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| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | NV SMX2 V100-32G |
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| uploaded Date | 10/28/2020 (month/day/year) | 10/28/2020 (month/day/year) |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| Dataset | CIFAR-10 | CIFAR-10 |
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| Training Parameters | epoch=125, steps=390, batch_size = 128, lr=0.1 | epoch=125, steps=390, batch_size=128, lr=0.1 |
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| Optimizer | SGD | SGD |
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@ -317,8 +318,28 @@ Parameters for both training and evaluation can be set in config.py
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| Model for inference | 21.50M (.onnx file), 21.60M(.air file) | |
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| Scripts | [googlenet script](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/googlenet) | [googlenet script](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/googlenet) |
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#### GoogleNet on 1200k images
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| Parameters | Ascend |
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| -------------------------- | ----------------------------------------------------------- |
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| Model Version | Inception V1 |
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| Resource | Ascend 910, CPU 2.60GHz, 56cores, Memory 314G |
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| uploaded Date | 10/28/2020 (month/day/year) |
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| MindSpore Version | 1.0.0 |
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| Dataset | 1200k images |
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| Training Parameters | epoch=300, steps=5000, batch_size=256, lr=0.1 |
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| Optimizer | Momentum |
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| Loss Function | Softmax Cross Entropy |
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| outputs | probability |
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| Loss | 2.0 |
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| Speed | 1pc: 152 ms/step; 8pcs: 171 ms/step |
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| Total time | 8pcs: 8.8 hours |
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| Parameters (M) | 13.0 |
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| Checkpoint for Fine tuning | 52M (.ckpt file) |
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| Scripts | [googlenet script](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/googlenet) |
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### Inference Performance
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#### GoogleNet on CIFAR-10
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| Parameters | Ascend | GPU |
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| ------------------- | --------------------------- | --------------------------- |
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@ -332,6 +353,18 @@ Parameters for both training and evaluation can be set in config.py
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| Accuracy | 1pc: 93.4%; 8pcs: 92.17% | 1pc: 93%, 8pcs: 92.89% |
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| Model for inference | 21.50M (.onnx file) | |
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#### GoogleNet on 1200k images
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| Parameters | Ascend |
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| ------------------- | --------------------------- |
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| Model Version | Inception V1 |
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| Resource | Ascend 910 |
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| Uploaded Date | 10/28/2020 (month/day/year) |
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| MindSpore Version | 1.0.0 |
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| Dataset | 1200k images |
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| batch_size | 256 |
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| outputs | probability |
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| Accuracy | 8pcs: 71.81% |
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## [How to use](#contents)
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### Inference
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