A CNN to distinguish between cat images and dog images with a fairly high accuracy with less data
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Updated
Jul 23, 2020 - Jupyter Notebook
A CNN to distinguish between cat images and dog images with a fairly high accuracy with less data
Image classification for dogs and cats with VGG-16 using PyTorch. Model accuracy: 99.6%. Classification API included
a simple CNN to classify a binary class consist of dogs and cats powered by TensorFlow.keras
A deep learning project built with PyTorch and deployed using Streamlit to classify images as either a cat or a dog.
An exploration of the trade-off between image quality and accuracy.
Image classifier for cats vs dogs using MobileNetV2 and TensorFlow/Keras
Deep learning model for Cats vs Dogs competition in Kaggle. Also it contains CNN's filters and feature maps visualizations.
This Repository contain algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. But computer will find it a bit more difficult.
A convolutional neural network (CNN)-based image classification project developed as part of the FreeCodeCamp Machine Learning with Python certification. The model is trained to distinguish between images of cats and dogs using TensorFlow/Keras.
A simple classifier model that fine-tune the InceptionV3 model
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