Scripts to set up a dataset and create a simple TensorFlow Image Classifier. These scripts work best with a CUDA supported Nvidia GPU.
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Dec 9, 2020 - Python
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mobilenetv2-model
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Developed recommendation pipelines leveraging content-based and collaborative filtering to present top n customer recommendations from user items and customer purchase histories. Alternatively, image similarity recommendations were generated using k means clustering and Neural Networks (NNs) from product images.
exploratory-data-analysis recommendation-system clustering-algorithm vgg16-model content-based-filtering colloborative-filtering mobilenetv2-model
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Jul 31, 2023 - Jupyter Notebook
A starter guide to implement pretrained MobileNetV2 model with local samples image testing
python jupyter-notebook image-processing image-classification beginner-friendly sample-project beginner-code beginners-guide mobilenetv2 google-colab google-colab-notebook pre-trained-model mobilenetv2-pytorch mobilenetv2-model
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Sep 13, 2025 - Jupyter Notebook
COVID-19-Xray-Dataset Deep Learning using 3 models
deep-learning metrics tensorflow keras warnings cnn-model loss-functions imagedatagenerator optimizer-algorithms mobilenetv2-model resnet50-model
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Oct 7, 2024 - Jupyter Notebook
Deep learning API that classifies fabrics (Cotton, Wool, etc.), textile patterns (Floral, Stripes, etc.), and extracts dominant colors from images.
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Sep 27, 2025 - Jupyter Notebook
Smart Vision Technology Quality Control
python opencv machine-learning django ocr hackathon tensorflow sqlite svm keras tesseract cnn support-vector-machine flipkart pytesseract mobilenetv2 flipkart-grid efficientdet mobilenetv2-model flipkartgrid
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Dec 21, 2024 - Python
This GitHub repository provides code implementations for a Drowsiness Detection Image Classification task, focusing on predicting whether eyes are open or closed. The dataset used in this project is designed for drowsiness detection and includes diverse eye movement recordings captured under controlled driving scenarios.
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Mar 7, 2024 - Jupyter Notebook
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