Our dataset consists of labeled images of dogs and cats, serving as the training material for the CNN to learn and generalize patterns. Ultimately, this CNN project exemplifies the power of deep learning in image classification tasks and serves as a foundatio...
import cv2 file_dir = "E:\\catanddog\\train0" save_dir ="E:\\catanddog\\train1" images = [] # 每张图片的路径组成的列表 temp = [] # 保存cat dog文件夹路径 for root, sub_folders, files in os.walk(file_dir): for name in files: images.append(os.path.join(root, name)) for ...
catanddog 喜爱 0 该数据集包含猫和狗的图片各200张,用于训练图像分类 Me_ZhangX zhang CC BY 4.0 图像分类 0 6 2023-05-10 详情 相关项目 评论(0) 创建项目 文件列表 train.zip train.zip (8.51M) 下载 File Name Size Update Time train/cat/cat.0.jpg 12414 2017-03-20 03:12:20 train/cat/...
This project demonstrates how to classify images of dogs and cats using a Convolutional Neural Network (CNN) implemented in PyTorch. The model can be trained on a dataset of dog and cat images and then used to classify new images or perform real-time predictions using a webcam ...
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
There are a number of different methods that can be used when estimating the size of the owned cat and dog population in a region, leading to varying population estimates. The aim of this study was to conduct a systematic review to evaluate the methods that have been used for estimating the...
In this study, a total of 518 dog and cat fracture tibia images that obtained from universities and institutions were used. F1 score value of this study on total dataset was found to be 85.8%. F1 score value of this study on dog dataset was found to be 87.8%. F1 ...
Processes and transforms image data into a suitable format for the classifier. Trains a model (if applicable) to distinguish between cat and dog images. Evaluates accuracy, providing insight into the model’s performance on the dataset. Visualization The notebook uses matplotlib to display original ...
Cat vs Dog Image Classifier 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 th...
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 ...