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!7865 modify README.md 

Merge pull request !7865 from wukesong/modify-readme
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10
model_zoo/official/cv/alexnet/README.md
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@ -30,6 +30,8 @@ AlexNet composition consists of 5 convolutional layers and 3 fully connected lay
# [Dataset](#contents)
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
- Dataset size：175M，60,000 32*32 colorful images in 10 classes
@ -195,15 +197,15 @@ Before running the command below, please check the checkpoint path used for eval
| -------------------------- | ------------------------------------------------------------| -------------------------------------------------|
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G | NV SMX2 V100-32G |
| uploaded Date | 06/09/2020 (month/day/year) | 17/09/2020 (month/day/year) |
| MindSpore Version | 0.5.0-beta | 0.7.0-beta |
| MindSpore Version | 1.0.0 | 0.7.0-beta |
| Dataset | CIFAR-10 | CIFAR-10 |
| Training Parameters | epoch=30, steps=1562, batch_size = 32, lr=0.002 | epoch=30, steps=1562, batch_size = 32, lr=0.002 |
| Optimizer | Momentum | Momentum |
| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy |
| outputs | probability | probability |
| Loss | 0.0016 | 0.01 |
| Speed | 21 ms/step | 16.8 ms/step |
| Total time | 17 mins | 14 mins |
| Loss | 0.08 | 0.01 |
| Speed | 7.3 ms/step | 16.8 ms/step |
| Total time | 6 mins | 14 mins |
| Checkpoint for Fine tuning | 445M (.ckpt file) | 445M (.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet |

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model_zoo/official/cv/lenet/README.md
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@ -30,6 +30,8 @@ LeNet is very simple, which contains 5 layers. The layer composition consists of
# [Dataset](#contents)
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
Dataset used: [MNIST](<http://yann.lecun.com/exdb/mnist/>)
- Dataset size：52.4M，60,000 28*28 in 10 classes
@ -165,16 +167,16 @@ You can view the results through the file "log.txt". The accuracy of the test da
| Parameters | LeNet |
| -------------------------- | ----------------------------------------------------------- |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
| uploaded Date | 06/09/2020 (month/day/year) |
| MindSpore Version | 0.5.0-beta |
| uploaded Date | 09/16/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | MNIST |
| Training Parameters | epoch=10, steps=1875, batch_size = 32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 0.002 |
| Speed | 1.70 ms/step |
| Total time | 43.1s | |
| Speed | 1.071 ms/step |
| Total time | 32.1s | |
| Checkpoint for Fine tuning | 482k (.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet |