Supported NNs
Supervisely suppports most of the state of the art models for common computer vision tasks:
Training, Inference, Pre-trained weights : off the shelf
All neural networks architectures (listed below) support both training and inference inside the Supervisely Platform. Also we provide pretrained weights for each architecture that can be used directly for inference or for transfer learning to speed up the training process on your custom data.
Source codes and customization
We aslo provide source codes for all neural networks so developers can customize them. Read more in the SDK chapter.
Interactive segmentation
Architecture |
Pretrained on |
Framework |
Paper |
Smart Tool |
Custom data |
PyTorch |
Medium |
|
Custom data + Supervisely Person |
|
Medium1, Medium2 |
Semantic segmentation
Architecture |
Pretrained on |
Framework |
Paper |
Unet V2 (based on VGG) |
ImageNet |
PyTorch |
arxiv |
PSPNet |
Cityscapes |
Tensorflow |
arxiv |
|
ADE20K |
|
|
ICNet |
Cityscapes |
Tensorflow |
arxiv |
|
ADE20K |
|
|
DeepLab V3 + |
Cityscapes |
Tensorflow |
arxiv |
|
ADE20K |
|
|
Instance segmentation
Architecture |
Backbone |
Pretrained on |
Framework |
Paper |
Mask-RCNN |
Inception V2 |
COCO |
Tensorflow |
arxiv |
|
ResNet-50 (atrous) |
COCO |
|
|
|
ResNet-101 (atrous) |
COCO |
|
|
Object detection
Architecture |
Backbone |
Pretrained on |
Framework |
Paper |
YOLO V3 |
|
COCO |
DarkNet (C++) |
arxiv |
SSD |
Inception V2 |
COCO |
Tensorflow |
arxiv |
|
MobileNet V1 |
COCO |
|
|
|
MobileNet V2 |
COCO |
|
|
|
MobileNet V2 (lite) |
COCO |
|
|
Faster-RCNN |
ResNet-50 |
COCO |
Tensorflow |
arxiv |
|
ResNet-101 |
COCO |
|
|
|
Inception V2 |
COCO |
|
|
|
Inception ResNet V2 (atrous) |
COCO |
|
|
|
NasNet |
COCO |
|
|
|
NasNet (lowproposals) |
COCO |
|
|
RFCN |
ResNet-101 |
COCO |
Tensorflow |
arxiv |
Classification
Architecture |
Backbone |
Pretrained on |
Framework |
Paper |
AlexNet |
|
ImageNet |
PyTorch |
arxiv |
VGG |
*-11 |
ImageNet |
PyTorch |
arxiv |
|
*-13 |
ImageNet |
|
|
|
*-16 |
ImageNet |
|
|
|
*-19 |
ImageNet |
|
|
|
*-11 with batchnorm |
ImageNet |
|
|
|
*-13 with batchnorm |
ImageNet |
|
|
|
*-16 with batchnorm |
ImageNet |
|
|
|
*-19 with batchnorm |
ImageNet |
|
|
ResNet |
*-18 |
ImageNet |
PyTorch |
arxiv |
|
*-34 |
ImageNet |
|
|
|
*-50 |
ImageNet |
|
|
|
*-101 |
ImageNet |
|
|
|
*-152 |
ImageNet |
|
|
SqueezeNet |
*-1.0 |
ImageNet |
PyTorch |
arxiv |
|
*-1.1 |
ImageNet |
|
|
Densenet |
*-121 |
ImageNet |
PyTorch |
arxiv |
|
*-169 |
ImageNet |
|
|
|
*-201 |
ImageNet |
|
|
|
*-161 |
ImageNet |
|
|
Inception V3 |
|
ImageNet |
PyTorch |
arxiv |
Text detection
Architecture |
Pretrained on |
Framework |
Paper |
EAST |
ICDAR 2015 |
Tensorflow |
arxiv |
CTPN |
ICDAR 2015 |
Tensorflow |
arxiv |
PixelLink |
ICDAR 2015 |
Tensorflow |
arxiv |
OCR
Architecture |
Pretrained on |
Framework |
Paper |
CNN-LSTM-CTC |
ICDAR 2015 |
Tensorflow |
arxiv |