New Gender/Age Classification Model Deployed

Here at PixLab, we recently deployed on production, a brand new gender/age classification model available to our customers via the FACEMOTION API endpoint.

gender age detection

  • The new model implementation is based on the ResNet-50 convolutional neural network (CNN) that is 50 layers deep. The network can easily classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

  • The reference, implementation paper is from: Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019 (

  • The Python/PHP code samples listed below should be able to easily output the age estimation, gender, and emotion pattern by just looking at the facial shape of any present human face in a given picture or video frame using our new classifcation model.

Python Code

  • FACEMOTION is the sole endpoint needed to perform such a task. It should output the rectangle coordinates for each detected human face that you can pass verbatim if desired to other processing endpoints like CROP or MOGRIFY plus the age estimation, gender and emotion pattern of the target face based on its facial shape.
  • Finally, all of our production ready, code samples are available to consult at our samples page or the PixLab Gihtub repository.

Announcing PixLab Annotate - An Online Image Annotation Tool

The PixLab Computer Vision team is pleased to announce the immediate availability of PixLab Annotate. A web based image annotation, labeling & segmentation tool for Machine Learning model training tasks and beyond...

Annotate Features Set:

  • Rectangle, Polygon, Zoom & Drag labeling tool.
  • Consistent JSON output accepted by most Machine Learning frameworks.
  • Optimized for instance segmentation (Mask R-CNN, etc).
  • Client-side persistent storage - No data transfer involved.
  • Persistent & easy label management (Create, Modify & Delete).
  • Full screen display & Snapshot capture.

Straightforward image segmentation and labeling thanks to the Rectangle & Polygon built-in tool!

At PixLab, we really believe that annotate is a great fit for data scientists, developers or students looking for a straightforward, online image segmentation and labeling tool for their daily machine learning model training tasks and beyond...

Annotate Homepage

Feature & Support Requests

Introducing the Pixel Generate API Endpoint

PixLab Logo

The PixLab Computer Vision Team is pleased to introduce the Pixel Generate API endpoint (/pixelgenerate) which let you in a single call, generate on the fly, images filled with random pixels of desired width & height using a mix of standard Image Processing and soon Machine Learning algorithms.

This endpoint is similar to /newimage except that the image contents is filled with random pixels. This is very useful for generating background (negative) samples for feeding Machine Learning training algorithms for example.

By default, this endpoint return a JSON object holding a link to the generated image output. But, you can set it via the Blob parameters to return the image binary contents instead.

Below, a Python snippet which generate on the fly a new image of height & width of 300x300 filled with random pixels using a single call to /pixelgenerate:

The code sample used to achieve such result is available to consult via the following Github link: