git clone https://github.com/shinta-anggreina/Cat-and-Dog-Classification.git Open the Jupyter Notebook or Google Colab. Run the model training script. Evaluate the model performance. 📄 Author Shinta Anggreina - Diponegoro University 🎯 Acknowledgment This project was developed in collaboration...
dataset.py model.py requirement.txt test.py train.py Repository files navigation README Apache-2.0 license classification-torch A simple demo of implementing cat and dog classification 这个项目是一个简单的使用resnet18实现猫狗分类的例子,主要学习:1)是神经网络实现分类的原理 2)了解模型优化流程:数据处理...
To avoid overfitting and create a larger dataset from a smaller one we can use a technique called data augmentation. This is simply performing random transofrmations on our images so that our model can generalize better. These transformations can be things like compressions, rotations, stretches a...
Even if you do not have any prior experience with deep learning, I urge you to come join me, and witness the many wonders of Deep Learning and CNN in particular. Chapters 00:00 - Dog vs Cat Classification Using Convolution Neural Network 01:29 - Tod...
Parsimony analyses under different weighting schemes and strengths were used to generate hypotheses of phylogenetic relationships. The phylogenetic analysis of 78 terminal taxa, using the combined dataset and weighting each column separately (SEP; k = 3) resulted in one most-parsimonious cladogram of...
I've made the FastAI's Cat vs Dog model into model that distinguishes lemons from limes and it all works fine in a notebook. I am now looking to transform this model into Core ML for my iOS app using TorchScript and Apple official guidelines for coremltools. ...
# 直接用PaddleHub提供的数据集 #dataset = hub.dataset.DogCat() from paddlehub.dataset.base_cv_dataset import BaseCVDataset class DemoDataset(BaseCVDataset): def __init__(self): # 数据集存放位置 self.dataset_dir = "dog-cat" super(DemoDataset, self).__init__( base_path=self.dataset_dir...
2018) framework model on 13,809 dog images annotated with 24 body landmarks. The article does not provide the accuracy of the landmark detection, however, using them, four classifiers with different architectures trained on 360 instances of body landmarks coordinates demonstrated classification ...
Added "binary-category" as a target type to the Oxford pet dataset. Uses the second numeric input in the annotation to get the species and outputs 0 for cat and 1 for dog. Discussed in #8364 . Notebook showing this in action can be found here
Compares the accuracy of KNN, HOG/SVM and CNN for classifying an image as cat or dog. Conclusion A CNN is the best approach to this dataset with a 91% accuracy. Neither the KNN or HOG/SVM performed well enough to be considered useable for this dataset as they barely did better than a...