RELEASE.md

Release Note

MindSpore Computer Vision 0.0.1

Models

mindcv.models now expose num_classes and in_channels as constructor arguments:

• Add DenseNet models and pre-trained weights
• Add GoogleNet models and pre-trained weights
• Add Inception V3 models and pre-trained weights
• Add Inception V4 models and pre-trained weights
• Add MnasNet models and pre-trained weights
• Add MobileNet V1 models and pre-trained weights
• Add MobileNet V2 models and pre-trained weights
• Add MobileNet V3 models and pre-trained weights
• Add ResNet models and pre-trained weights
• Add ShuffleNet V1 models and pre-trained weights
• Add ShuffleNet V2 models and pre-trained weights
• Add SqueezeNet models and pre-trained weights
• Add VGG models and pre-trained weights
• Add ViT models and pre-trained weights

Dataset

mindcv.data now expose:

• Add Mnist dataset
• Add FashionMnist dataset
• Add Imagenet dataset
• Add CIFAR10 dataset
• Add CIFAR100 dataset

Loss

mindcv.loss now expose:

• Add BCELoss
• Add CrossEntropyLoss

Optimizer

mindcv.optim now expose:

• Add SGD optimizer
• Add Momentum optimizer
• Add Adam optimizer
• Add AdamWeightDecay optimizer
• Add RMSProp optimizer
• Add Adagrad optimizer
• Add Lamb optimizer

Learning_Rate Scheduler

mindcv.scheduler now expose:

• Add WarmupCosineDecay learning rate scheduler
• Add ExponentialDecayLR learning rate scheduler
• Add Constant learning rate scheduler

Release

mindcv-0.0.1.apk

mindcv-0.0.1-py3-none-any.whl.sha256

mindcv-0.0.1-py3-none-any.whl