deep-learningpytorchmnistimagenetimage-classificationresnetsqueezenetaircraftsknowledge-distillationcifar100mobilenetshufflenetfine-grained-classificationvggface2cub-200-2011tiny-imagenetcars196flowers102stanford-dogs UpdatedOct 7, 2022 Python Challenge: Apply advanced computer vision concepts (CNN only) and beat st...
vgg_16.py This paper by Karen Simonyan and Andrew Zisserman introduced the VGG-16 architecture. The authors reached state-of-the-art performance using only a deep stack of 3x3xC filters and max-pooling layers. Because Tiny ImageNet has much lower resolution than the original ImageNet data, ...
For the discriminator network, we employ the VGG19 [22] model pre-trained on ImageNet as our backbone network and we replace all the f c layers with two parallel f c lay- ers. The f c layers are initialized by a zero-mean Gaussian distribution with standard deviation 0.1, and all ...
was originally developed to tackle the semantic segmentation challenges encountered in medical image analysis. It is named after its U-shaped architecture. However, the definition of U-Net has become quite broad nowadays. Many models with a U-shaped design are commonly referred to as “U-Net...
Full size image Model complexity comparison Table 6 shows the comparison of the model complexity of our proposed MwdpNet and comparison networks, including parameters, computations, and training time. VGG16 has the most parameters (138.7 M) and computations (15.47GFLOPS/s), resulting in longer tra...
ResNet101 performs slightly better than ResNet50 and much better than VGG16. Importantly, our VGG16-based model already outperforms prior art by a large margin on "hard" set. 500x500 regions per image (to take advantage of batch pro- cessing). We fine-tune pre-trained ImageNet models ...
对于共享的CNN,我们尝试了ResNet101,ResNet50和VGG16。试验结果如下: 简而言之,这篇论文具有一定的新颖性,在尺度、分辨率与上下文做了很多实验与探讨,为人脸检测的研究提供了新思路。不知道您在看完之后,有没有新想法,欢迎与我一起探讨。 原文链接: Tiny Face Detector, CVPR 2017 Finding Tiny Faces 编辑于 20...
在流行的CNN检测器中,来自特征提取器(例如VGG,Resnet)的特征图将同时具有定位和分类准确性。我们使用L2重新表达让学生网络从教师网络中学习特征图,并利用Li等人[4]联合训练版本作为我们的支柱。与尺寸等于类别数量的软目标[35]不同,特征图的尺寸与输入和网络结构的大小有关。有时数字可能是数百万。简单地模仿整个特...
This directory includes code and data to train a Tiny VGG model (inspired by the demo CNN in Stanford CS231n class) on 10 everyday classes from the Tiny ImageNet. Installation First, you want to unzip data.zip. The file structure would be something like: .├── data │ ├── class...
We introduce VGGKANs - an VGG-like models with KAN convolutions instead of regular ones. Main class VGG could be found models/vggkan.py. The model supports all types of KANs Convolutional layers. Pretrained on Imagenet1k ckeckpoints: ModelAccuracy, top1Accuracy, top5AUC (ovo)AUC (ovr) VG...