frame

Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Sign In

Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Survey: Request for customer feedback

Please feel free to add comments if you choose "other" for any of the following questions.

Survey: Request for customer feedback
  1. What Deep Learning Framework do you use?47 votes
    1. TensorFlow
      51.06%
    2. Caffe
      21.28%
    3. PyTorch
        6.38%
    4. Caffe2
        0.00%
    5. CNTK
        0.00%
    6. MXNet
        8.51%
    7. Chainer
        4.26%
    8. Other
        8.51%
  2. What use cases are you most interested in?47 votes
    1. Image Classification
      12.77%
    2. Object Detection
      38.30%
    3. Image Segmentation
        8.51%
    4. Audio Classification
        0.00%
    5. Natural Language Processing
        2.13%
    6. Deep Reinforcement Learning
        2.13%
    7. Chat Bot
        4.26%
    8. Stereo Depth
        0.00%
    9. Image Captioning
        0.00%
    10. SLaM
        0.00%
    11. Face Detection and Recognition
      23.40%
    12. Other
        8.51%
  3. What Networks do you commonly work with?47 votes
    1. MobileNet
      10.64%
    2. VGG
        4.26%
    3. Inception
        8.51%
    4. Inception-ResNet
        8.51%
    5. ResNet
        6.38%
    6. Faster RCNN
        6.38%
    7. SSD
      14.89%
    8. Yolo
      12.77%
    9. LSTM
        4.26%
    10. Other
      23.40%

Comments

  • 3 Comments sorted by Votes Date Added
  • An IMPORTANT user feedback is better documentation. The dev team please put sometime for that stuff. At the moment, NCS is great but poorly documented, and the Docs website is not properly constructed, like information is scattered and put anywhere possible. A neat and concise doc like those of Tensorflow or Keras or MxNet is greatly appreciated. Thanks!

  • Support of ONNX in the future would be nice.

  • What use cases are you most interested in? I think this question falls into what is the common
    terminology of the day issue.
    Yolo segments the image and classifys the objects in the image, that checks multiple boxes as does
    what Tensorflow calls this Tensorflow Object Detection API
    Is that what you are asking when you say Object detection? But isn't Tensorflows Object detection break down into segmenting
    then classifying the image segments of image giving the same result as YOLO?
    Or what do you call classifications?

Sign In or Register to comment.