v 0.11.0 (09/05/2023)¶
Highlights¶
Support EasyCV as a plug-in for [modelscope](https://github.com/modelscope/modelscope.
v 0.10.0 (06/03/2023)¶
Highlights¶
Support STDC, STGCN, ReID and Multi-len MOT.
Support multi processes for predictor data preprocessing. For the model with more time consuming in data preprocessing, the speedup can reach more than 50%.
New Features¶
Improvements¶
Speed up inference for face detector when using mtcnn. (#273)
Add mobilenet config for itag and imagenet dataset, and optimize
ClsSourceImageList
api to support string label. (#276) (#283)Support multi-rows replacement for first order parameter. (#282)
Add a tool to convert itag dataset to raw dataset. (#290)
Add
PoseTopDownPredictor
to replaceTorchPoseTopDownPredictorWithDetector
(#296)
v 0.8.0 (5/12/2022)¶
Highlights¶
New Features¶
Improvements¶
Unify the parsing method of config scripts, and support both local and pai platform products (#235)
Add more data source apis for open source datasets, involving classification, detection, segmentation and keypoints tasks. And part of the data source apis support automatic download. For more information, please refer to data_hub (#206 #229)
Add confusion matrix metric for Classification models (#241)
Add prediction script (#239)
v 0.7.0 (3/11/2022)¶
Highlights¶
New Features¶
Support semantic mask2former (#199)
Support face 2d keypoint detection (#191)
Support hand keypoints detection (#191)
Support wholebody keypoint detection (#207)
Support auto hyperparameter optimization of NNI (#211)
Add DeiT III (#171)
Add semantic segmentation model SegFormer (#191)
Add 3d detection model BEVFormer (#203)
Improvements¶
v 0.2.2 (07/04/2022)¶
initial commit & first release
SOTA SSL Algorithms
EasyCV provides state-of-the-art algorithms in self-supervised learning based on contrastive learning such as SimCLR, MoCO V2, Swav, DINO and also MAE based on masked image modeling. We also provides standard benchmark tools for ssl model evaluation.
Vision Transformers
EasyCV aims to provide plenty vision transformer models trained either using supervised learning or self-supervised learning, such as ViT, Swin-Transformer and XCit. More models will be added in the future.
Functionality & Extensibility
In addition to SSL, EasyCV also support image classification, object detection, metric learning, and more area will be supported in the future. Although convering different area, EasyCV decompose the framework into different componets such as dataset, model, running hook, making it easy to add new compoenets and combining it with existing modules. EasyCV provide simple and comprehensive interface for inference. Additionaly, all models are supported on PAI-EAS, which can be easily deployed as online service and support automatic scaling and service moniting.
Efficiency
EasyCV support multi-gpu and multi worker training. EasyCV use DALI to accelerate data io and preprocessing process, and use fp16 to accelerate training process. For inference optimization, EasyCV export model using jit script, which can be optimized by PAI-Blade.