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!4666 fix yolov3_darknet53_quant&yolov3-resenet18&ssd net README bug 

Merge pull request !4666 from chengxb7532/master
This commit is contained in:
mindspore-ci-bot 2020-08-18 20:01:08 +08:00 committed by Gitee
parent e5285bbb30 6bee09303d
commit b32c5c551e
3 changed files with 5 additions and 5 deletions
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2
model_zoo/official/cv/ssd/README.md
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@ -66,7 +66,7 @@ To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will ge
sh run_distribute_train.sh 8 500 0.2 coco /data/hccl.json
```
The input parameters are device numbers, epoch size, learning rate, dataset mode and [hccl json configuration file](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). **It is better to use absolute path.**
The input parameters are device numbers, epoch size, learning rate, dataset mode and [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). **It is better to use absolute path.**
You will get the loss value of each step as following:

6
model_zoo/official/cv/yolov3_darknet53_quant/README.md
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@ -71,7 +71,7 @@ sh run_distribute_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_10
sh run_standalone_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_102400.ckpt
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
> About rank_table.json, You can generate it by using the [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
#### Result
@ -108,14 +108,14 @@ epoch[134], iter[86500], loss:34.303755, 145.18 imgs/sec, lr:1.6245529650404933e
```
# infer
sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [DEVICE_ID]
```
#### Launch
```bash
# infer with checkpoint
sh run_eval.sh dataset/coco2014/ checkpoint/0-135.ckpt
sh run_eval.sh dataset/coco2014/ checkpoint/0-131.ckpt 0
```

2
model_zoo/official/cv/yolov3_resnet18/README.md
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@ -51,7 +51,7 @@ To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and
sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json
```
The input variables are device numbers, epoch size, mindrecord directory path, dataset directory path, train TXT file path and [hccl json configuration file](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). **It is better to use absolute path.**
The input variables are device numbers, epoch size, mindrecord directory path, dataset directory path, train TXT file path and [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). **It is better to use absolute path.**
You will get the loss value and time of each step as following: