diff --git a/model_zoo/official/cv/ssd/README.md b/model_zoo/official/cv/ssd/README.md
index f658f582dd..0d9ee3c707 100644
--- a/model_zoo/official/cv/ssd/README.md
+++ b/model_zoo/official/cv/ssd/README.md
@@ -31,6 +31,8 @@ SSD discretizes the output space of bounding boxes into a set of default boxes o
The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.
# [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: [COCO2017]()
- Dataset size：19G
@@ -299,14 +301,14 @@ mAP: 0.2244936111705981
| -------------------------- | -------------------------------------------------------------| -------------------------------------------------------------|
| Model Version | SSD V1 | SSD V1 |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | NV SMX2 V100-16G |
-| uploaded Date | 06/01/2020 (month/day/year) | 09/24/2020 (month/day/year) |
-| MindSpore Version | 0.3.0-alpha | 1.0.0 |
+| uploaded Date | 09/15/2020 (month/day/year) | 09/24/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | COCO2017 | COCO2017 |
| Training Parameters | epoch = 500, batch_size = 32 | epoch = 800, batch_size = 32 |
| Optimizer | Momentum | Momentum |
| Loss Function | Sigmoid Cross Entropy,SmoothL1Loss | Sigmoid Cross Entropy,SmoothL1Loss |
| Speed | 8pcs: 90ms/step | 8pcs: 121ms/step |
-| Total time | 8pcs: 4.81hours | 8pcs: 12.31hours |
+| Total time | 8pcs: 4.81hours | 8pcs: 12.31hours |
| Parameters (M) | 34 | 34 |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd |
@@ -317,8 +319,8 @@ mAP: 0.2244936111705981
| ------------------- | ----------------------------| ----------------------------|
| Model Version | SSD V1 | SSD V1 |
| Resource | Ascend 910 | GPU |
-| Uploaded Date | 06/01/2020 (month/day/year) | 09/24/2020 (month/day/year) |
-| MindSpore Version | 0.3.0-alpha | 1.0.0 |
+| Uploaded Date | 09/15/2020 (month/day/year) | 09/24/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | COCO2017 | COCO2017 |
| batch_size | 1 | 1 |
| outputs | mAP | mAP |
diff --git a/model_zoo/official/cv/yolov3_darknet53/README.md b/model_zoo/official/cv/yolov3_darknet53/README.md
index de93846d96..77f701ce8d 100644
--- a/model_zoo/official/cv/yolov3_darknet53/README.md
+++ b/model_zoo/official/cv/yolov3_darknet53/README.md
@@ -40,6 +40,7 @@ YOLOv3 use DarkNet53 for performing feature extraction, which is a hybrid approa
# [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: [COCO2014](https://cocodataset.org/#download)
@@ -307,8 +308,8 @@ The above python command will run in the background. You can view the results th
| -------------------------- | ----------------------------------------------------------- |------------------------------------------------------------ |
| Model Version | YOLOv3 |YOLOv3 |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G | NV SMX2 V100-16G; CPU 2.10GHz, 96cores; Memory, 251G |
-| uploaded Date | 06/31/2020 (month/day/year) | 09/02/2020 (month/day/year) |
-| MindSpore Version | 0.5.0-alpha | 0.7.0 |
+| uploaded Date | 09/15/2020 (month/day/year) | 09/02/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | COCO2014 | COCO2014 |
| Training Parameters | epoch=320, batch_size=32, lr=0.001, momentum=0.9 | epoch=320, batch_size=32, lr=0.001, momentum=0.9 |
| Optimizer | Momentum | Momentum |
@@ -328,8 +329,8 @@ The above python command will run in the background. You can view the results th
| ------------------- | --------------------------- |------------------------------|
| Model Version | YOLOv3 | YOLOv3 |
| Resource | Ascend 910 | NV SMX2 V100-16G |
-| Uploaded Date | 06/31/2020 (month/day/year) | 08/20/2020 (month/day/year) |
-| MindSpore Version | 0.5.0-alpha | 0.7.0 |
+| Uploaded Date | 09/15/2020 (month/day/year) | 08/20/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | COCO2014, 40,504 images | COCO2014, 40,504 images |
| batch_size | 1 | 1 |
| outputs | mAP | mAP |
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/README.md b/model_zoo/official/cv/yolov3_darknet53_quant/README.md
index 21b2e00c9e..83f9c15249 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/README.md
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/README.md
@@ -42,6 +42,7 @@ YOLOv3 use DarkNet53 for performing feature extraction, which is a hybrid approa
# [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: [COCO2014](https://cocodataset.org/#download)
@@ -276,8 +277,8 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558
| -------------------------- | ---------------------------------------------------------------------------------------------- |
| Model Version | YOLOv3_Darknet53_Quant V1 |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G |
-| uploaded Date | 06/31/2020 (month/day/year) |
-| MindSpore Version | 0.6.0-alpha |
+| uploaded Date | 09/15/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 |
| Dataset | COCO2014 |
| Training Parameters | epoch=135, batch_size=16, lr=0.012, momentum=0.9 |
| Optimizer | Momentum |
@@ -297,8 +298,8 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558
| ------------------- | --------------------------- |
| Model Version | YOLOv3_Darknet53_Quant V1 |
| Resource | Ascend 910 |
-| Uploaded Date | 06/31/2020 (month/day/year) |
-| MindSpore Version | 0.6.0-alpha |
+| Uploaded Date | 09/15/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 |
| Dataset | COCO2014, 40,504 images |
| batch_size | 1 |
| outputs | mAP |
diff --git a/model_zoo/official/cv/yolov3_resnet18/README.md b/model_zoo/official/cv/yolov3_resnet18/README.md
index c4dbfbf868..78646862ea 100644
--- a/model_zoo/official/cv/yolov3_resnet18/README.md
+++ b/model_zoo/official/cv/yolov3_resnet18/README.md
@@ -34,6 +34,7 @@ And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResN
# [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: [COCO2017]()
@@ -200,35 +201,35 @@ Note the precision and recall values are results of two-classification(person an
### Evaluation Performance
-| Parameters | Ascend |
+| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | YOLOv3_Resnet18 V1 |
-| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
-| uploaded Date | 06/01/2020 (month/day/year) |
-| MindSpore Version | 0.2.0-alpha |
-| Dataset | COCO2017 |
+| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
+| uploaded Date | 09/15/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 |
+| Dataset | COCO2017 |
| Training Parameters | epoch = 150, batch_size = 32, lr = 0.001 |
-| Optimizer | Adam |
+| Optimizer | Adam |
| Loss Function | Sigmoid Cross Entropy |
-| outputs | probability |
-| Speed | 1pc: 120 ms/step; 8pcs: 160 ms/step |
-| Total time | 1pc: 150 mins; 8pcs: 70 mins |
-| Parameters (M) | 189 |
+| outputs | probability |
+| Speed | 1pc: 120 ms/step; 8pcs: 160 ms/step |
+| Total time | 1pc: 150 mins; 8pcs: 70 mins |
+| Parameters (M) | 189 |
| Scripts | [yolov3_resnet18 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18) | [yolov3_resnet18 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18) |
### Inference Performance
-| Parameters | Ascend |
+| Parameters | Ascend |
| ------------------- | ----------------------------------------------- |
-| Model Version | YOLOv3_Resnet18 V1 |
+| Model Version | YOLOv3_Resnet18 V1 |
| Resource | Ascend 910 |
-| Uploaded Date | 06/01/2020 (month/day/year) |
-| MindSpore Version | 0.2.0-alpha |
+| Uploaded Date | 09/15/2020 (month/day/year) |
+| MindSpore Version | 1.0.0 |
| Dataset | COCO2017 |
| batch_size | 1 |
| outputs | presion and recall |
-| Accuracy | class 0: 88.18%/66.00%; class 1: 85.34%/79.13% |
+| Accuracy | class 0: 88.18%/66.00%; class 1: 85.34%/79.13% |
# [Description of Random Situation](#contents)