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cats-vs-dogs

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This project uses OpenAI's CLIP model and a Fisher algorithm to classify cat and dog images. The dataset is split into training and testing sets, embeddings are extracted with CLIP, and the Fisher algorithm is applied to classify the images. Performance is evaluated with accuracy, precision, recall, and F1 score, visualized with a confusion matrix.

  • Updated May 24, 2024
  • Python

Repository for a deep learning model that classifies images as either cats or dogs using deep learning techniques. The model is trained on a diverse dataset and achieves high accuracy in distinguishing between these two popular pet categories. Includes pre-processing scripts, model architecture, and evaluation metrics for seamless implementation

  • Updated Feb 24, 2024
  • Jupyter Notebook

Find App Link below. This project involves using CNNs to predict facial landmarks on images of cat faces. It utilizes Python, OpenCV, TensorFlow, and Keras libraries for image processing, modeling, and training. The ResNet50 architecture is employed as the base model, augmented with dense layers for facial landmark prediction

  • Updated Aug 15, 2023
  • Jupyter Notebook

Project Name: "FurFlix 🐱🐶: Classifying Cats and Dogs with CNN" Description: This project utilizes Convolutional Neural Networks (CNN) to build a model for classifying images of cats and dogs with an impressive 80% accuracy.

  • Updated Jun 6, 2023
  • Jupyter Notebook

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