2000 transformed Images (224x224) from original Cats vs Dogs dataset Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more

Apr 16, 2017 · More parameters may lead to several problems, first you'll need more computing power. Then you may need more data to train on, since a lot of parameters and not enough samples may lead to overfitting, specially with CNNs. The choice for a 224 from AlexNet also allowed them to apply some data augmentation.

By alternately applying attention inner patch and between patches, we implement cross attention to maintain the performance with lower computational cost and build a hierarchical network called Cross Attention Transformer(CAT) for other vision tasks.

Use transfer learning to easily classify dog and cat pictures with a 98.5% accuracy. Transfer learning. In this article, you will learn how to use transfer learning for powerful image recognition, with keras, TensorFlow, and state-of-the-art pre-trained …

Slightly improved cat-dataset for use in cat face landmark prediction models. Dataset consists of cat images with face landmarks annotated. It was created with this project in mind.

Dataset: microsoft/cats_vs_dogs; Training/Validation split: 80/20; Input size: 224x224 RGB images; Trained for 10 epochs; Best validation accuracy: 93.25%; Intended uses Image classification between cats and dogs; Transfer learning base for similar pet/animal classification tasks; Limitations Only trained on cats and dogs; May not perform well on:

Dec 26, 2020 · The cat-dog image dataset had 8,000 training images and 2,000 testing images representing an 80/20 train test split. Each image had a 224x224x3 shape representing a 224x224 image size across...

Data Collection: Used the "Dogs vs. Cats" dataset available in the Fastai library. Data Preprocessing: Resized images to a uniform size of 224x224 pixels and split the dataset into training and validation sets.

在本教程中，我们将介绍如何使用ONNX将PyTorch中定义的模型转换为ONNX格式，然后将其加载到Caffe2中。 一旦进入Caffe2，我们就可以运行模型来仔细检查它是否正确导出，然后我们展示了如何使用Caffe2功能 (如移动导出器）在移动设备上执行模型。 在本教程中，你需要安装onnx和Caffe2。 您可以使用pip install onnx获取onnx的二进制版本。 注意: 本教程需要PyTorch master分支，可以按照 这里 说明进行安装。 超分辨率是一种提高图像，视频分辨率的方法，广泛用于 …

The 'shape bias' dataset was introduced in Geirhos et al. (ICLR 2019) and consists of 224x224 images with conflicting texture and shape information (e.g., cat shape with elephant texture). This is used to measure the shape vs. texture bias of image classifiers.