From c28eaa7fa8483bbd56a375abcaadbbf0fc86c8dd Mon Sep 17 00:00:00 2001 From: CaoJian Date: Wed, 28 Oct 2020 17:08:27 +0800 Subject: [PATCH] update googlenet README --- model_zoo/official/cv/googlenet/README.md | 25 ++++++++++++----------- 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/model_zoo/official/cv/googlenet/README.md b/model_zoo/official/cv/googlenet/README.md index a7e5e21851..d5f59e9d47 100644 --- a/model_zoo/official/cv/googlenet/README.md +++ b/model_zoo/official/cv/googlenet/README.md @@ -41,6 +41,7 @@ Specifically, the GoogleNet contains numerous inception modules, which are conne # [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]() @@ -167,11 +168,11 @@ Parameters for both training and evaluation can be set in config.py 'image_width': 224 # image width used as input to the model 'data_path': './cifar10' # absolute full path to the train and evaluation datasets 'device_target': 'Ascend' # device running the program - 'device_id': 4 # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training + 'device_id': 0 # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training 'keep_checkpoint_max': 10 # only keep the last keep_checkpoint_max checkpoint 'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt' # the absolute full path to save the checkpoint file 'onnx_filename': 'googlenet.onnx' # file name of the onnx model used in export.py - 'geir_filename': 'googlenet.geir' # file name of the geir model used in export.py + 'air_filename': 'googlenet.air' # file name of the air model used in export.py ``` For more configuration details, please refer the script `config.py`. @@ -265,7 +266,7 @@ For more configuration details, please refer the script `config.py`. Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "username/googlenet/train_parallel0/train_googlenet_cifar10-125_48.ckpt". The accuracy of the test dataset will be as follows: ``` - # grep "accuracy: " dist.eval.log + # grep "accuracy: " eval.log accuracy: {'acc': 0.9217} ``` @@ -310,8 +311,8 @@ For more configuration details, please refer the script `config.py`. | -------------------------- | ----------------------------------------------------------- | ---------------------- | | Model Version | Inception V1 | Inception V1 | | Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | NV SMX2 V100-32G | -| uploaded Date | 08/31/2020 (month/day/year) | 08/20/2020 (month/day/year) | -| MindSpore Version | 0.7.0-alpha | 0.6.0-alpha | +| uploaded Date | 10/28/2020 (month/day/year) | 10/28/2020 (month/day/year) | +| MindSpore Version | 1.0.0 | 1.0.0 | | Dataset | CIFAR-10 | CIFAR-10 | | Training Parameters | epoch=125, steps=390, batch_size = 128, lr=0.1 | epoch=125, steps=390, batch_size=128, lr=0.1 | | Optimizer | Momentum | Momentum | @@ -323,15 +324,15 @@ For more configuration details, please refer the script `config.py`. | Parameters (M) | 13.0 | 13.0 | | Checkpoint for Fine tuning | 43.07M (.ckpt file) | 43.07M (.ckpt file) | | Model for inference | 21.50M (.onnx file), 21.60M(.air file) | | -| Scripts | [googlenet script](https://gitee.com/mindspore/mindspore/tree/r0.7/model_zoo/official/cv/googlenet) | [googlenet script](https://gitee.com/mindspore/mindspore/tree/r0.6/model_zoo/official/cv/googlenet) | +| Scripts | [googlenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet) | [googlenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet) | #### GoogleNet on 1200k images | Parameters | Ascend | | -------------------------- | ----------------------------------------------------------- | | Model Version | Inception V1 | | Resource | Ascend 910, CPU 2.60GHz, 56cores, Memory 314G | -| uploaded Date | 09/20/2020 (month/day/year) | -| MindSpore Version | 0.7.0-alpha | +| uploaded Date | 10/28/2020 (month/day/year) | +| MindSpore Version | 1.0.0 | | Dataset | 1200k images | | Training Parameters | epoch=300, steps=5000, batch_size=256, lr=0.1 | | Optimizer | Momentum | @@ -352,8 +353,8 @@ For more configuration details, please refer the script `config.py`. | ------------------- | --------------------------- | --------------------------- | | Model Version | Inception V1 | Inception V1 | | Resource | Ascend 910 | GPU | -| Uploaded Date | 08/31/2020 (month/day/year) | 08/20/2020 (month/day/year) | -| MindSpore Version | 0.7.0-alpha | 0.6.0-alpha | +| Uploaded Date | 10/28/2020 (month/day/year) | 10/28/2020 (month/day/year) | +| MindSpore Version | 1.0.0 | 1.0.0 | | Dataset | CIFAR-10, 10,000 images | CIFAR-10, 10,000 images | | batch_size | 128 | 128 | | outputs | probability | probability | @@ -365,8 +366,8 @@ For more configuration details, please refer the script `config.py`. | ------------------- | --------------------------- | | Model Version | Inception V1 | | Resource | Ascend 910 | -| Uploaded Date | 09/20/2020 (month/day/year) | -| MindSpore Version | 0.7.0-alpha | +| Uploaded Date | 10/28/2020 (month/day/year) | +| MindSpore Version | 1.0.0 | | Dataset | 1200k images | | batch_size | 256 | | outputs | probability |