The Dogs vs. Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or cat.Our basic task is to create an algorithm to classify whether an image contains a dog or a cat. The input for this task is images of dogs or cats from training dataset, while the output is the classification accuracy on test dataset.[1]
- pip
>> pip install -r requirement.txt
- Conda
>> conda env create -f environment.yml
>> conda activate cats_and_dogs
>> cd data
>> bash download.sh
>> python main.py -h
usage: main.py [-h] [-file_dir FILE_DIR] [-batch_size BATCH_SIZE] [-lr LR]
[-epoch EPOCH] [-model {SIMPLE,DEEPER}]
#Example
>> python main.py -epoch 10 -model 'DEEPER'
>> tensorboard --logdir='TensorBoard/runs/'
