{"id":24603795,"url":"https://github.com/aryansinghiiit/imagedetection","last_synced_at":"2026-04-20T19:32:09.793Z","repository":{"id":187740332,"uuid":"677486984","full_name":"AryanSinghIIIT/ImageDetection","owner":"AryanSinghIIIT","description":"A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.  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I created a small demo that classifies two image data sets - cat and dog images, and returns a prediction score denoting the possibility of it being an image of a cat or a dog.\n\n\u003cbr/\u003e\n\n## Installation\nMake sure you have [Python 3](https://www.python.org/downloads/) installed, then install [Tensorflow](https://www.tensorflow.org/install/) on your system, and clone this repo.\n\n\u003cbr/\u003e\n\n## Usage\n\n### Prepare the image data sets\nIn order to start the transfer learning process, a folder named ``training_dataset`` needs to be created in the root of the project folder. This folder will contain the image data sets for all the subjects, for whom the classification is to be performed.\n\nCreate the ``training_dataset`` folder and add the images for all the data sets in the following manner -\n\n```javascript\n/\n|\n|ar\n---- /training_dataset\n|    |\n|    |\n|    ---- /cat\n|    |    cat1.jpg\n|    |    cat2.jpg\n|    |    ...\n|    |\n|    |\n|    ---- /dog\n|         dog1.jpg\n|         dog2.jpg\n|         ...\n|\n|     \n```\nThis enables classification of images between the ``cat`` and ``dog`` data sets.\n\n\u003e Make sure to include multiple variants of the subject (side profiles, zoomed in images etc.), the more the images, the better is the result.\n\n### Initiate transfer learning\nGo to the project directory and run -\n\n```javascript\n$ bash train.sh\n```\nThis script installs the ``Inception`` model and initiates the re-training process for the specified image data sets.\n\nOnce the process is complete, it will return a training accuracy somewhere between ``85% - 100%``.\n\nThe ``training summaries``, ``retrained graphs`` and ``retrained labels`` will be saved in a folder named ``tf_files``.\n\n### Classify objects\n![file-dialog1](https://github.com/AryanSinghIIIT/ImageDetection/assets/96579866/d3c30694-5f9d-41a9-b938-b14917206934)\n\n```javascript\npython3 classify.py\n```\n\nThis opens up the file dialog using which you can select your input file.\n\n\nOnce the input file is selected, the classifier will output the predictions for each data set. A prediction score between ``0.8`` to ``1`` is considered to be optimal.\n![cli-output1](https://github.com/AryanSinghIIIT/ImageDetection/assets/96579866/3169ceb3-0639-4d9c-b00c-1e7224ebe7e6)\n\n\n\u003cbr/\u003e\n\n## Results\n![result](https://github.com/AryanSinghIIIT/ImageDetection/assets/96579866/6f6fcd40-8038-4070-a4e2-2bdda92e8069)\n\n\u003cbr/\u003e\n\n\n\n\u003cbr/\u003e\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryansinghiiit%2Fimagedetection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryansinghiiit%2Fimagedetection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryansinghiiit%2Fimagedetection/lists"}