T-2022.06-RC1

This is T-2022.06 release candidate 1 of the Synopsys-caffe-models, a set of Caffe Deep Learning Models adapted for use with DesignWare EV Processors.

These models must be used together with Synopsys-Caffe T-2022.06 and the MetaWare EV Development Toolkit T-2022.06 from Synopsys.

Supported Models

Alexnet, DAN, Denoiser, Densenet, Deeplab, Facedetect v1/v2, FaceNet, Faster_rcnn_resnet101, FCN, Googlenet. ICNet,
Inception_Resnet v1/v2, Inception v1/v2/v3/v4, LeNet, Mobilenet, Mobilenet_ssd, mtcnn v1, OpenPose, PSPNet,
Resnet 50/101/152, Resnet50_ssd, ResNext 50/101/152, RetinaNet, SegNet, ShuffleNet v1/v2, SRGAN, SqueezeNet, SRCNN, SSD,
PSPNet, UNet, VDCR, VGG16, Yolo v1/v1_tiny/v2_coco/v2_voc/v2_tiny/v3/v3_tiny

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 2021.12

Updated the following graphs:

• added MT-CNN version mtcnn-transp-pnet_optimized.prototxt and ResNet version ResNet-50-deploy-1280x720.prototxt