Which is the version of espnetv2 for pytorch?

Once installed, run the following commands in your terminal to verify the version: This should print something like this 3.4.3. You will see that EESP unit, the core building block of the ESPNetv2 architecture, has a for loop to process the input at different dilation rates. You can parallelize it using Streams in PyTorch.
Similarly, how to check the version of PyTorch in another environment?
Similar to pip, if you used Anaconda to install PyTorch. you can use the command conda list to check its detail which also include the version info. You you want to check in another environment, e.g., pytorch14 below, use -n like this: What is PyTorch?
Besides, is the GitHub repository for espnetv2 obsolete? IMPORTANT NOTE 2 (7 June, 2019): This repository is obsolete and we are not maintaining it anymore. This repository contains the source code of our paper, ESPNetv2 which is accepted for publication at CVPR'19. Note: New segmentation models for the PASCAL VOC and the Cityscapes are coming soon.
Furthermore, what kind of convolutions does espnetv2 use?
The ESPNetv2network extends the ESPNet net- work [32] using these efficient forms of convolutions. To learn representations from a large effective receptive field, ESPNetv2uses depth-wise “dilated” separable convolu- tions instead of depth-wise separable convolutions.
Also, which is the version of PyTorch in CUDA?
For example, 1.9.0+cu102 means the PyTorch version is 1.9.0, and the CUDA version is 10.2. Alternatively, use your favorite Python IDE or code editor and run the same code.

3 Similar Question Found

Which is the source code of espnetv2 paper?

This repository contains the source code of our paper, ESPNetv2 which is accepted for publication at CVPR'19. Note: New segmentation models for the PASCAL VOC and the Cityscapes are coming soon.

Is the github repository for espnetv2 obsolete?

IMPORTANT NOTE 2 (7 June, 2019): This repository is obsolete and we are not maintaining it anymore. This repository contains the source code of our paper, ESPNetv2 which is accepted for publication at CVPR'19. Note: New segmentation models for the PASCAL VOC and the Cityscapes are coming soon.

Which is more power efficient yolov2 or espnetv2?

Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets.