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Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?



- Download the frozen model (.pbâ??-â??protobuf) and load it into memory
- Use the built in helper code to load labels, categories, visualization tools etc.
- Open a new session and run the model on an image

- Use the VideoFileClip function to extract images from the video
- The fl_image function is an awesome function that can take an image and replace it with a modified image. I used this to run object detection on every image extracted from the video
- Finally all the modified clip images were combined into a new video

- Try the more accurate but high overhead models and see how much of a difference they make
- Find out ways of speeding up the API, so it can be used for real time object detection on a mobile device
- Google also provides the ability to use these models for transfer learning i.e load the frozen models and add another output layer with different image categories