Release Note

0.3.0 (2024/1/17)

Release 0.3.0 is published. We will drop MindSpore 1.x in the future release.

1. New models:
   • Y-16GF of RegNet
   • SwinTransformerV2
   • VOLO
   • CMT
   • HaloNet
   • SSD
   • DeepLabV3
   • CLIP & OpenCLIP
2. Features:
   • AsymmetricLoss & JSDCrossEntropy
   • Augmentations Split
   • Customized AMP
3. Bug fixes:
   • Since the classifier weights are not fully deleted, you may encounter an error passing in the num_classes when creating a pre-trained model.
4. Refactoring:
   • The names of many models have been refactored for better understanding.
   • Script of VisionTransformer.
   • Script of Mixed(PyNative+jit) mode training.
5. Documentation:
   • A guide of how to extract multiscale features from backbone.
   • A guide of how to finetune the pre-trained model on a custom dataset.
6. BREAKING CHANGES:
   • We are going to drop support of MindSpore 1.x for it's EOL.
   • Configuration filter_bias_and_bn will be removed and renamed as weight_decay_filter, due to a prolonged misunderstanding of the MindSpore optimizer. We will migrate the existing training recipes, but the signature change of function a will be incompatible and the old version training recipes will also be incompatible. See PR/752 for details.

0.2.2 (2023/6/16)

1. New version 0.2.2 is released! We upgrade to support MindSpore v2.0 while maintaining compatibility of v1.8
2. New models:
   • ConvNextV2
   • mini of CoAT
   • 1.3 of MnasNet
   • AMP(O3) version of ShuffleNetV2
3. New features:
   • Gradient Accumulation
   • DynamicLossScale for customized TrainStep
   • OneCycleLR and CyclicLR learning rate scheduler
   • Refactored Logging
   • Pyramid Feature Extraction
4. Bug fixes:
   • Serving Deployment Tutorial(mobilenet_v3 doesn't work on ms1.8 when using Ascend backend)
   • Some broken links on our documentation website.

0.2.1

• 2023/6/2

1. New version: 0.2.1 is released!
2. New documents is online!

• 2023/5/30

1. New Models:
   • AMP(O2) version of VGG
   • GhostNet
   • AMP(O3) version of MobileNetV2 and MobileNetV3
   • (x,y)_(200,400,600,800)mf of RegNet
   • b1g2, b1g4 & b2g4 of RepVGG
   • 0.5 of MnasNet
   • b3 & b4 of PVTv2
2. New Features:
   • 3-Augment, Augmix, TrivialAugmentWide
3. Bug Fixes:
   • ViT pooling mode

• 2023/04/28

1. Add some new models, listed as following
   • VGG
   • DPN
   • ResNet v2
   • MnasNet
   • MixNet
   • RepVGG
   • ConvNeXt
   • Swin Transformer
   • EdgeNeXt
   • CrossViT
   • XCiT
   • CoAT
   • PiT
   • PVT v2
   • MobileViT
2. Bug fix:
   • Setting the same random seed for each rank
   • Checking if options from yaml config exist in argument parser
   • Initializing flag variable as Tensor in Optimizer Adan

0.2.0

• 2023/03/25

1. Update checkpoints for pretrained ResNet for better accuracy
   • ResNet18 (from 70.09 to 70.31 @Top1 accuracy)
   • ResNet34 (from 73.69 to 74.15 @Top1 accuracy)
   • ResNet50 (from 76.64 to 76.69 @Top1 accuracy)
   • ResNet101 (from 77.63 to 78.24 @Top1 accuracy)
   • ResNet152 (from 78.63 to 78.72 @Top1 accuracy)
2. Rename checkpoint file name to follow naming rule ({model_scale-sha256sum.ckpt}) and update download URLs.

• 2023/03/05

1. Add Lion (EvoLved Sign Momentum) optimizer from paper https://arxiv.org/abs/2302.06675
   • To replace adamw with lion, LR is usually 3-10x smaller, and weight decay is usually 3-10x larger than adamw.
2. Add 6 new models with training recipes and pretrained weights for
   • HRNet
   • SENet
   • GoogLeNet
   • Inception V3
   • Inception V4
   • Xception
3. Support gradient clip
4. Arg name use_ema changed to ema, add ema: True in yaml to enable EMA.

0.1.1

• 2023/01/10

1. MindCV v0.1 released! It can be installed via PyPI pip install mindcv now.
2. Add training recipe and trained weights of googlenet, inception_v3, inception_v4, xception

0.1.0

• 2022/12/09

1. Support lr warmup for all lr scheduling algorithms besides cosine decay.
2. Add repeated augmentation, which can be enabled by setting --aug_repeats to be a value larger than 1 (typically, 3 or 4 is a common choice).
3. Add EMA.
4. Improve BCE loss to support mixup/cutmix.

• 2022/11/21

1. Add visualization for loss and acc curves
2. Support epochwise lr warmup cosine decay (previous is stepwise)

• 2022/11/09

1. Add 7 pretrained ViT models.
2. Add RandAugment augmentation.
3. Fix CutMix efficiency issue and CutMix and Mixup can be used together.
4. Fix lr plot and scheduling bug.

• 2022/10/12

1. Both BCE and CE loss now support class-weight config, label smoothing, and auxiliary logit input (for networks like inception).

0.0.1-beta

• 2022/09/13

1. Add Adan optimizer (experimental)

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