This project uses tensorflow and a coin sorter to identify and sort pennies based on their visibile information. This project is based on object detection models and builds an entire pipeline to support detecting pennies.
The image loader is a uses the Google API and a custome search engine to find images using a query. This script executes the query then downloads all of the results to the workspace 'images' folder.
This script uses the Google API and requires an API key and Custom Search Engine ID.
Setup the Google API Key and Custom Search Engine:
- Create an API project
- Enable Custom Search API
- Generate API key credentials
- Setup the custome search engine and in the web form where you create/edit your custom search engine enable "Image search" option and for "Sites to search" option select "Search the entire web but emphasize included sites".
Warning the usage of the custom search engine is free but highly limited.
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annotations: This folder will be used to store all *.csv files and the respective TensorFlow *.record files, which contain the list of annotations for our dataset images.
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exported-models: This folder will be used to store exported versions of our trained model(s).
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images: This folder contains a copy of all the images in our dataset, as well as the respective *.xml files produced for each one, once labelImg is used to annotate objects.
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images/train: This folder contains a copy of all images, and the respective *.xml files, which will be used to train our model.
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images/test: This folder contains a copy of all images, and the respective *.xml files, which will be used to test our model.
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models: This folder will contain a sub-folder for each of training job. Each subfolder will contain the training pipeline configuration file *.config, as well as all files generated during the training and evaluation of our model.
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pre-trained-models: This folder will contain the downloaded pre-trained models, which shall be used as a starting checkpoint for our training jobs.