Releases: foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models
MWEV2018.12.RC1
This is release 2018.12.RC1 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with Synopsys-Caffe v2018.12.RC1 and the MetaWare EV Development Toolkit v2018.12.RC1 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- icnet
- inception_resnet_v1
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- mobilenet_ssd
- openpose
- pspnet
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- squeezenet
- ssd
- unet
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
- yolo_v3 (yolo_v3_tiny included)
Images
- imagenet_mean - mean images for different image sizes
- imagenet_test_images - simple set of test images
- images - different image data sub-sets
Changes vs v2018.09
New models
- openpose
- segnet
- pspnet
- unet
- yolo_v3 (yolo_v3_tiny included)
Improved parts
Added improved compressed, pruned and random-pruned models
Helper tools
git_sparse_download.sh(bat) - helps to download just part of models.
MWEV2018.09.RC2
This is release 2018.09.RC2 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with Synopsys-Caffe v2018.09.RC2 and the MetaWare EV Development Toolkit v2018.09.RC2 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- icnet
- inception_resnet_v1
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- mobilenet_ssd
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- squeezenet
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
Changes vs v2018.06
New models
- icnet
- inception_resnet_v1
- mobilenet_ssd
- pvanet
Improved models
- alexnet
- squezenet - instead of SqueezeNet_v1.0 SqueezeNet_v1.1
- denoizer
- facedetect_v1
- googlenet_cnn
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- mobilenet
- resnet_101
- resnet_152
- resnet_50
- ssd
- vgg16
- yolo_v2_voc
Removed models
- scene_segmentation
Images
- imagenet_mean - mean images for different image sizes
- imagenet_test_images - simple set of test images
- images - different image data sub-sets
Helper tools
git_sparse_download.sh(bat) - helps to download just part of models.
MWEV2018.09
This is release 2018.09 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with Synopsys-Caffe v2018.09 and the MetaWare EV Development Toolkit v2018.09 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- icnet
- inception_resnet_v1
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- mobilenet_ssd
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- squeezenet
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
Changes vs v2018.06
New models
- icnet
- inception_resnet_v1
- mobilenet_ssd
- pvanet
Improved models
- alexnet
- squezenet - instead of SqueezeNet_v1.0 SqueezeNet_v1.1
- denoizer
- facedetect_v1
- googlenet_cnn
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- mobilenet
- resnet_101
- resnet_152
- resnet_50
- ssd
- vgg16
- yolo_v2_voc
Removed models
- scene_segmentation
Images
- imagenet_mean - mean images for different image sizes
- imagenet_test_images - simple set of test images
- images - different image data sub-sets
Helper tools
git_sparse_download.sh(bat) - helps to download just part of models.
MWEV2018.09.RC1
This is release 2018.09.RC1 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with Synopsys-Caffe v2018.09.RC1 and the MetaWare EV Development Toolkit v2018.09.RC1 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- icnet
- inception_resnet_v1
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- mobilenet_ssd
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- Squeeze_Netv1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
Changes vs v2018.06
New models
- icnet
- inception_resnet_v1
- mobilenet_ssd
- pvanet
Improved models
- alexnet
- SqueezeNet_v1.0
- SqueezeNet_v1.1)
- denoizer
- facedetect_v1
- googlenet_cnn
- inception_resnet_v2
- inception_v1
- inception_v2
- inception_v3
- inception_v4
- mobilenet
- resnet_101
- resnet_152
- resnet_50
- ssd
- vgg16
- yolo_v2_voc
Removed models
- scene_segmentation
Images
- imagenet_mean - mean images for different image sizes
- imagenet_test_images - simple set of test images
- images - different image data sub-sets
MWEV2018.06
This is release 2018.06 RC3 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with synopsys-caffe and the MetaWare EV Development Toolkit v2018.06 RC3 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- imagenet_test_images
- inception_resnet_v1
- inception_resnet_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- scene_segmentation
- Squeeze_Net_v1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
New models vs v2018.03
- faster_rcnn_resnet101
- inception_resnet_v1
- inception_resnet_v2
- resnet_101_cnn
- resnet_152_cnn
Fixed models vs v2018.03
- inception_resnet_v2
- inception_v3
- inception_v4
- mobilnet
- resnet_50
Usage Instructions
IMPORTANT NOTE: This repository uses git-lfs for large file storage. You can't use zip and tar files listed in the "Assets" section above (added by default by github). You must clone the repository using the instructions below
-
Install git-lfs
-
Ensure git-lfs and git versions you use are compatible (equal or greater than below)
$ git lfs version
git-lfs/2.0.2 # or newer
$ git –version
git version 2.9.3 # or newer
-
Add SSH key to your GitHub account (if you haven't already)
-
clone the repo:
$ git clone https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models.git
2018.09-ENG-180824
This is release 2018.09-ENG-180824 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with synopsys-caffe and the MetaWare EV Development Toolkit v2018.06 RC3 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- imagenet_test_images
- inception_resnet_v1
- inception_resnet_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- scene_segmentation
- Squeeze_Net_v1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
New models vs v2018.03
- faster_rcnn_resnet101
- inception_resnet_v1
- inception_resnet_v2
- resnet_101_cnn
- resnet_152_cnn
Fixed models vs v2018.03
- inception_resnet_v2
- inception_v3
- inception_v4
- mobilnet
- resnet_50
Usage Instructions
IMPORTANT NOTE: This repository uses git-lfs for large file storage. You can't use zip and tar files listed in the "Assets" section above (added by default by github). You must clone the repository using the instructions below
-
Install git-lfs
-
Ensure git-lfs and git versions you use are compatible (equal or greater than below)
$ git lfs version
git-lfs/2.0.2 # or newer
$ git –version
git version 2.9.3 # or newer
-
Add SSH key to your GitHub account (if you haven't already)
-
clone the repo:
$ git clone https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models.git
MWEV2018.06RC2
This is release 2018.06 RC2 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with synopsys-caffe and the MetaWare EV Development Toolkit v2018.06 RC2 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- imagenet_test_images
- inception_resnet_v1
- inception_resnet_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- scene_segmentation
- Squeeze_Net_v1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
New models vs v2018.03
- faster_rcnn_resnet101
- inception_resnet_v1
- inception_resnet_v2
- resnet_101_cnn
- resnet_152_cnn
Fixed models vs v2018.03
- inception_resnet_v2
- inception_v3
- inception_v4
- mobilnet
- resnet_50
Usage Instructions
IMPORTANT NOTE: This repository uses git-lfs for large file storage. You can't use zip and tar files listed in the "Assets" section above (added by default by github). You must clone the repository using the instructions below
-
Install git-lfs
-
Ensure git-lfs and git versions you use are compatible (equal or greater than below)
$ git lfs version
git-lfs/2.0.2 # or newer
$ git –version
git version 2.9.3 # or newer
-
Add SSH key to your GitHub account (if you haven't already)
-
clone the repo:
$ git clone https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models.git
Caffe Models for DesignWare EV Processors, v2018.06 RC1
This is release 2018.06 RC1 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with synopsys-caffe and the MetaWare EV Development Toolkit v2018.06 RC1 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- faster_rcnn_resnet101
- googlenet
- imagenet_test_images
- inception_resnet_v1
- inception_resnet_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- resnet_101_cnn
- resnet_152_cnn
- resnet_50
- scene_segmentation
- Squeeze_Net_v1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
New models vs v2018.03
- faster_rcnn_resnet101
- inception_resnet_v1
- inception_resnet_v2
- resnet_101_cnn
- resnet_152_cnn
Fixed models vs v2018.03
- inception_resnet_v2
- inception_v3
- inception_v4
- mobilnet
- resnet_50
Usage Instructions
IMPORTANT NOTE: This repository uses git-lfs for large file storage. You can't use zip and tar files listed in the "Assets" section above (added by default by github). You must clone the repository using the instructions below
-
Install git-lfs
-
Ensure git-lfs and git versions you use are compatible (equal or greater than below)
$ git lfs version
git-lfs/2.0.2 # or newer
$ git –version
git version 2.9.3 # or newer
-
Add SSH key to your GitHub account (if you haven't already)
-
clone the repo:
$ git clone https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models.git
Caffe Models for DesignWare EV Processors, v2018.03 RC2
This is RC2 for 2018.03 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with synopsys-caffe and the MetaWare EV Development Toolkit v2018.03 RC2, from Synopsys.
Supported Models
- alexnet
- denoiser
- dencenet
- facedetect_v1
- facedetect_v2
- googlenet
- imagenet_test_images - new imagenet test images
- inception_resnet_v2 - new
- inception_v3 - new
- inception_v4 - new
- lenet
- mobilnet - new put all mobilnet models together in one folder
- resnet_50
- scene_segmentation
- Squeeze_Net_v1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
Usage Instructions
IMPORTANT NOTE: This repository uses git-lfs for large file storage. You can't use zip and tar files listed in the "Assets" section above (added by default by github). You must clone the repository using the instructions below
-
Install git-lfs
-
Ensure git-lfs and git versions you use are compatible (equal or greater than below)
$ git lfs version
git-lfs/2.0.2
$ git –version
git version 2.9.3
-
Add SSH key to your GitHub account (if you haven't already)
-
clone the repo:
$ git clone https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models.git
Caffe Models for DesignWare EV Processors, v2018.03
This is release 2018.03 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV6x Processors.
These models must be used together with synopsys-caffe and the MetaWare EV Development Toolkit v2018.03 from Synopsys.
Supported Models
- alexnet
- denoiser
- densenet
- facedetect_v1
- facedetect_v2
- googlenet
- imagenet_test_images
- inception_resnet_v2
- inception_v3
- inception_v4
- lenet
- mobilenet
- resnet_50
- scene_segmentation
- Squeeze_Net_v1.0
- SqueezeNet_v1.1
- ssd
- vgg16
- yolo_tiny
- yolo_v1
- yolo_v2_coco
- yolo_v2_voc
New models vs v2017.12
- densenet
- inception_resnet_v2
- inception_v3
- inception_v4
- mobilenet
- ssd
Usage Instructions
IMPORTANT NOTE: This repository uses git-lfs for large file storage. You can't use zip and tar files listed in the "Assets" section above (added by default by github). You must clone the repository using the instructions below
-
Install git-lfs
-
Ensure git-lfs and git versions you use are compatible (equal or greater than below)
$ git lfs version
git-lfs/2.0.2 # or newer
$ git –version
git version 2.9.3 # or newer
-
Add SSH key to your GitHub account (if you haven't already)
-
clone the repo:
$ git clone https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models.git