From 15f3ed4e07fc791a1dbe21ca905af09441cc2db8 Mon Sep 17 00:00:00 2001 From: chengxianbin Date: Tue, 27 Oct 2020 19:26:47 +0800 Subject: [PATCH] modify mindspore version about ssd&yolov3 README --- model_zoo/official/cv/ssd/README.md | 12 ++++--- .../official/cv/yolov3_darknet53/README.md | 9 +++--- .../cv/yolov3_darknet53_quant/README.md | 9 +++--- .../official/cv/yolov3_resnet18/README.md | 31 ++++++++++--------- 4 files changed, 33 insertions(+), 28 deletions(-) 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)