# 使用的是https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/master/pytorch_classification/Test11_efficientnetV2/model.py 中的代码!fromcollectionsimportOrderedDictfromfunctoolsimportpartialfromtyp
网络搭建,训练验证测试全部代码):https://blog.csdn.net/qq_31417941/article/details/97915035模型:EfficientNetV2网络详解:https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_classification/Test11_efficientnet
3.2 建立预训练模型我们将要使用的预训练模型是torchvision.models.efficientnet_b0().我们将要创建的示例...
pytorch_classification ConfusionMatrix ConvNeXt MobileViT Test10_regnet Test11_efficientnetV2 Test1_official_demo Test2_alexnet Test3_vggnet Test4_googlenet Test5_resnet Test6_mobilenet Test7_shufflenet Test8_densenet Test9_efficientNet analyze_weights_featuremap ...
optimizer pytorch imagenet image-classification resnet pretrained-models mixnet pretrained-weights distributed-training mobilenet-v2 mobile-deep-learning mobilenetv3 efficientnet augmix randaugment nfnets normalization-free-training vision-transformer-models convnext maxvit Resources Readme License Apache-2.0 ...
“Macro” average refers to a method of calculating average performance in multiclass or multilabel classification problems, which treats all classes equally. To summarize, for the medical multi-label dataset, we are using the following: Pre-trainedefficientnet_v2_smodel. ...
test_set = datasets.MNIST(root='./data', train=False, transform=transform) 类似于训练集加载方式,train=False表明此处加载的是测试集。 (6)创建测试数据加载器: test_loader = DataLoader(test_set, batch_size=64, shuffle=False) 为测试数据集创建一个DataLoader,shuffle=False通常用于测试数据,因为在测试...
| 模型方面 | (efficientnet/resnest/seresnext等) | 1 | | 数据增强 | (旋转/镜像/对比度等、mixup/cutmix) | 2 | | 损失函数 | (交叉熵/focal_loss等) | 3| | 模型部署 | (flask/grpc/BentoML等) | [4] (https://github.com/MachineLP/PyTorch_image_classifier/tree/master/serving)| | onn...
pytorch实现EfficientNetV2pytorchfcn 需要准备的第三方库:numpy、os、torch、cv2一、Dataload.py的编写该部分的主要工作是完成数据的预处理、训练集测试集的划分以及数据集的读取,即得到train_dataloader、test_dataloader;数据预处理首先是数据的预处理部分,由于FCN不限制输入图片的尺寸大小,所以预处理部分较为精简,只需要...
'efficientnet_b2_pruned','efficientnet_b3','efficientnet_b3_pruned','efficientnet_b4','efficientnet_el','efficientnet_el_pruned','efficientnet_em','efficientnet_es','efficientnet_es_pruned','efficientnet_lite0','efficientnetv2_rw_m','efficientnetv2_rw_s','ens_adv_inception_resnet_v2','ese_...