Release Note
0.2.2 (2023/6/16)
- New version
0.2.2
is released! We upgrade to support MindSpore
v2.0 while maintaining compatibility of v1.8
- New models:
- New features:
- Gradient Accumulation
- DynamicLossScale for customized TrainStep
- OneCycleLR and CyclicLR learning rate scheduler
- Refactored Logging
- Pyramid Feature Extraction
- 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
- New version:
0.2.1
is released!
- New documents is online!
- New Models:
- New Features:
- 3-Augment, Augmix, TrivialAugmentWide
- Bug Fixes:
- Add some new models, listed as following
- 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
- 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)
- Rename checkpoint file name to follow naming rule ({model_scale-sha256sum.ckpt}) and update download URLs.
- 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.
- Add 6 new models with training recipes and pretrained weights for
- Support gradient clip
- Arg name
use_ema
changed to ema
, add ema: True
in yaml to enable EMA.
0.1.1
- MindCV v0.1 released! It can be installed via PyPI
pip install mindcv
now.
- Add training recipe and trained weights of googlenet, inception_v3, inception_v4, xception
0.1.0
- Support lr warmup for all lr scheduling algorithms besides cosine decay.
- 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).
- Add EMA.
- Improve BCE loss to support mixup/cutmix.
- Add visualization for loss and acc curves
- Support epochwise lr warmup cosine decay (previous is stepwise)
- Add 7 pretrained ViT models.
- Add RandAugment augmentation.
- Fix CutMix efficiency issue and CutMix and Mixup can be used together.
- Fix lr plot and scheduling bug.
- Both BCE and CE loss now support class-weight config, label smoothing, and auxiliary logit input (for networks like inception).
0.0.1-beta
- 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