FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. A summary of the steps for optimizing and deploying a model that was trained with the TensorFlow* framework: Configure the Model Optimizer for TensorFlow* (TensorFlow was used to train your model). Using Mask R-CNN, we can automatically compute pixel-wise masks for objects in the image, allowing us to segment the foreground from the background.. An example mask computed via Mask R-CNN can be seen in Figure 1 at the top of this section.. On the top-left, we have an input image … Search also for Single Shot Object Detecion (SSD) and Faster-RCNN to … Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Mask R-CNN is a state-of-the-art deep neural network architecture used for image segmentation. Exercises and Solutions to accompany Sutton's Book and David Silver's course. ; Freeze the TensorFlow model if your model is not already frozen or skip this step and use the instruction to a convert a non-frozen model. - dennybritz/reinforcement-learning Implementation of Reinforcement Learning Algorithms. The YOLO V3 is indeed a good solution and is pretty fast. AttributeError: module 'tensorflow.python.framework.ops' has no attribute '_TENSOR_LIKE_TYPES' @danhnguyen12091998 You should check the module "tensorflow.python.framework.ops" for the correct name of the attribute. The FaceNet system can be used broadly thanks to multiple third-party open source implementations of You wont need tensorflow if you just want to load and use the trained models (try Keras if you need to train the models to make things simpler). Python, OpenAI Gym, Tensorflow.

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