PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. The work...
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more - taylor1355/pytorch-image-models
Github项⽬rwightman/pytorch-image-models整合了PyTorch的许多图像模型、脚本和预训练参 数。项⽬中的模型主要有下⾯三个来源：Torchvision https://github.com/pytorch/vision/tree/master/torchvision/models Cadene's Pretrained Models https://github.com/Cadene/pretrained-models.pytorch 作者⾃⼰提供：http...
链接: https://github.com/rwightman/pytorch-image-models
作者同样给出训练的实验结果,然后还有使用方法,同样也是在指定位置准备好数据,就可以使用了。 另外,作者还给出 ToDo 列表,会继续完善该项目。 链接: https:///rwightman/pytorch-image-models 最后,祝大家端午安康! 欢迎关注我的微信公众号--算法猿的成长,或者扫描下方的二维码,大家一起交流,学习和进步!
PyTorch-TabNet: Attentive Interpretable Tabular Learning 2.教程 3.可视化 Loss Visualization Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps ...
models import resnet50from vit_pytorch.distill import DistillableViT, DistillWrapper teacher = resnet50(pretrained = True) v = DistillableViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 8, mlp_dim = 2048, dropout = 0.1, emb_dropout =...
import torchfrom torchvision.models import resnet50from vit_pytorch.distill import DistillableViT, DistillWrapperteacher = resnet50(pretrained = True)v = DistillableViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 8, mlp_...
models import resnet50 from vit_pytorch.distill import DistillableViT, DistillWrapper teacher = resnet50(pretrained = True) v = DistillableViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 8, mlp_dim = 2048, dropout = 0.1, emb_dropout ...
项目地址：https://github.com/pytorch/text 2.pytorch-seq2seq：在 PyTorch 中实现序列到序列（seq2seq）模型的框架。项目地址：https://github.com/IBM/pytorch-seq2seq 3.anuvada：PyTorch 中实现的 NLP 的可解释模型。项目地址：https://github.com/Sandeep42/anuvada 4.audio：PyTorch 的简单音频 I/O。...