本次网络模型选用resnet34(论文链接:Deep Residual Learning for Image Recognition) model.py文件完整代码如下: import torch import torch.nn as nn class ResBlock(nn.Module): def __init__(self, in_channel, out_channel): super(ResBlock, self).__init__() self.conv1 = nn.Conv2d(in_chan...
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 foundation...
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 ...
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 Dataset The dataset should be structured as follows: This structure represents the organization of your dataset: data/ is the main directory. ...
filename = os.path.join(save_dir, 'catvsdog_path_dataset.tfrecords') print(filename) n_samples = len(label_list) writer = tf.python_io.TFRecordWriter(filename) print('\n开始制作数据集...') for i in np.arange(0, n_samples): ...
importosfromPILimportImagefromtorch.utilsimportdataimportnumpy as npimporttorchvision.transforms as TclassDogCat(data.Dataset):#获取所有图片地址,并根据训练、验证、测试划分数据def__init__(self, root, transforms = None, train = True, test =False): ...
https://www.freecodecamp.org/learn/machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier For this challenge, you will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63...
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
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...
“Image pre-processing” section introduces the image pre-processing steps of the pet dog expression dataset. “Network architecture and applied strategies” section provides the detail information about the designed CNN model architecture. “Whale optimization algorithm and improvement strategies” section ...