Image classificationLS-EfficientNetRemote sensingSC-CNN algorithmRecently, researchers have proposed a lot of deep convolutional neural network (CNN) approaches with obvious flaws to tackle the difficult semantic classification (SC) task of remote sensing images (RSI). In this pa...
Image classification on fashion-MNIST I would like to share my results (93.43% accuracy on average) on the fashion-MNIST dataset. You can find further informations about the dataset on Zalando Research and Kaggle This dataset is a great option instead of using traditional handwritten MNIST. Thanks...
SimCC首先使用卷积神经网络(CNN)或基于transformer的主干来提取关键点表示。给定获得的关键点表示,然后SimCC分别对垂直和水平坐标进行坐标分类,以产生最终预测。为了减少量化误差,SimCC将每个像素均匀的划分为几个bin,从而实现亚像素定位精度。请注意,与可能引入多个反卷积层的基于heatmap方法不同,SimCC只需要两个轻量级...
#Simple-CNN-classification Unzip the folder resized.zip befor running the code. The original data files exceed the limit to be uploaded. You can modify the code and use the resized images. About No description, website, or topics provided. Resources Readme Activity Stars 0 stars Watchers...
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2. On the image classification task, using the pre-trained Vision Transformer (ViT) [4] as the skeleton network, R-Drop fine-tuned the CIFAR-100 dataset and the ImageNet dataset, after which the ViT-B/16 and ViT-L/16 models have achieved s...
CNN本质上其实是模版匹配,不管是预测body part area还是keypoint area,它们都有比较符合视觉感官的表征。回归gaussian peak比直接回归两个相邻关键点之间的offset更加容易;另外,热图编码本身就包含了定位信息,这样我们就避免了多任务学习(classification 和offet regression)之间的平衡麻烦。
(CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in...
This repository contains the architectures, Models, logs, etc pertaining to the SimpleNet Paper (Lets keep it simple: Using simple architectures to outperform deeper architectures ) deep-learning pytorch imagenet image-classification convolutional-neural-networks cnn-model fast-net cnn-pytorch simplenet ...
We have studied the combination of local and global prediction results in order to determine the loss function of a neural network and thus to make better use of the information contained in each image for achieving better classification results for the CG forensics problem; We have carried out ...