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In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical networks and some of the related pre-trained models. If you have needs for the model zoo, you can file an issue on gitee or MindSpore, We will consider it in time.
SOTA models using the latest MindSpore APIs
The best benefits from MindSpore
Officially maintained and supported
Domain | Sub Domain | Network | Ascend | GPU | CPU |
---|---|---|---|---|---|
Computer Vision (CV) | Image Classification | AlexNet | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | CNN | ✅ | ||
Computer Vision (CV) | Image Classification | DenseNet100 | ✅ | ||
Computer Vision (CV) | Image Classification | DenseNet121 | ✅ | ||
Computer Vision (CV) | Image Classification | DPN | ✅ | ||
Computer Vision (CV) | Image Classification | EfficientNet-B0 | ✅ | ||
Computer Vision (CV) | Image Classification | GoogLeNet | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | InceptionV3 | ✅ | ||
Computer Vision (CV) | Image Classification | InceptionV4 | ✅ | ||
Computer Vision (CV) | Image Classification | LeNet | ✅ | ✅ | ✅ |
Computer Vision (CV) | Image Classification | LeNet (Quantization) | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | MobileNetV1 | ✅ | ||
Computer Vision (CV) | Image Classification | MobileNetV2 | ✅ | ✅ | ✅ |
Computer Vision (CV) | Image Classification | MobileNetV2 (Quantization) | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | MobileNetV3 | ✅ | ||
Computer Vision (CV) | Image Classification | NASNet | ✅ | ||
Computer Vision (CV) | Image Classification | ResNet-18 | ✅ | ||
Computer Vision (CV) | Image Classification | ResNet-50 | ✅ | ✅ | ✅ |
Computer Vision (CV) | Image Classification | ResNet-50 (Quantization) | ✅ | ||
Computer Vision (CV) | Image Classification | ResNet-101 | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | ResNeXt50 | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | SE-ResNet50 | ✅ | ||
Computer Vision (CV) | Image Classification | ShuffleNetV1 | ✅ | ||
Computer Vision (CV) | Image Classification | ShuffleNetV2 | ✅ | ||
Computer Vision (CV) | Image Classification | SqueezeNet | ✅ | ||
Computer Vision (CV) | Image Classification | Tiny-DarkNet | ✅ | ||
Computer Vision (CV) | Image Classification | VGG16 | ✅ | ✅ | |
Computer Vision (CV) | Image Classification | Xception | ✅ | ||
Computer Vision (CV) | Object Detection | CenterFace | ✅ | ||
Computer Vision (CV) | Object Detection | CTPN | ✅ | ||
Computer Vision (CV) | Object Detection | Faster R-CNN | ✅ | ✅ | |
Computer Vision (CV) | Object Detection | Mask R-CNN | ✅ | ||
Computer Vision (CV) | Object Detection | Mask R-CNN (MobileNetV1) | ✅ | ||
Computer Vision (CV) | Object Detection | RetinaFace-ResNet50 | ✅ | ||
Computer Vision (CV) | Object Detection | SSD | ✅ | ✅ | ✅ |
Computer Vision (CV) | Object Detection | SSD-MobileNetV1-FPN | ✅ | ||
Computer Vision (CV) | Object Detection | SSD-Resnet50-FPN | ✅ | ||
Computer Vision (CV) | Object Detection | SSD-VGG16 | ✅ | ||
Computer Vision (CV) | Object Detection | WarpCTC | ✅ | ✅ | |
Computer Vision (CV) | Object Detection | YOLOv3-ResNet18 | ✅ | ||
Computer Vision (CV) | Object Detection | YOLOv3-DarkNet53 | ✅ | ✅ | |
Computer Vision (CV) | Object Detection | YOLOv3-DarkNet53 (Quantization) | ✅ | ||
Computer Vision (CV) | Object Detection | YOLOv4 | ✅ | ||
Computer Vision (CV) | Text Detection | DeepText | ✅ | ||
Computer Vision (CV) | Text Detection | PSENet | ✅ | ||
Computer Vision (CV) | Text Recognition | CNN+CTC | ✅ | ||
Computer Vision (CV) | Semantic Segmentation | DeepLabV3 | ✅ | ✅ | |
Computer Vision (CV) | Semantic Segmentation | U-Net2D (Medical) | ✅ | ||
Computer Vision (CV) | Semantic Segmentation | U-Net3D (Medical) | ✅ | ||
Computer Vision (CV) | Semantic Segmentation | U-Net++ | ✅ | ||
Computer Vision (CV) | Keypoint Detection | OpenPose | ✅ | ||
Computer Vision (CV) | Keypoint Detection | SimplePoseNet | ✅ | ||
Computer Vision (CV) | Optical Character Recognition | CRNN | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | BERT | ✅ | ✅ | |
Natural Language Processing (NLP) | Natural Language Understanding | FastText | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | GNMT v2 | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | GRU | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | MASS | ✅ | ✅ | |
Natural Language Processing (NLP) | Natural Language Understanding | SentimentNet | ✅ | ✅ | ✅ |
Natural Language Processing (NLP) | Natural Language Understanding | Transformer | ✅ | ✅ | |
Natural Language Processing (NLP) | Natural Language Understanding | TinyBERT | ✅ | ✅ | |
Natural Language Processing (NLP) | Natural Language Understanding | TextCNN | ✅ | ||
Recommender | Recommender System, CTR prediction | DeepFM | ✅ | ✅ | ✅ |
Recommender | Recommender System, Search, Ranking | Wide&Deep | ✅ | ✅ | |
Recommender | Recommender System | NAML | ✅ | ||
Recommender | Recommender System | NCF | ✅ | ||
Graph Neural Networks (GNN) | Text Classification | GCN | ✅ | ||
Graph Neural Networks (GNN) | Text Classification | GAT | ✅ | ||
Graph Neural Networks (GNN) | Recommender System | BGCF | ✅ |
Domain | Sub Domain | Network | Ascend | GPU | CPU |
---|---|---|---|---|---|
Computer Vision (CV) | Image Classification | FaceAttributes | ✅ | ||
Computer Vision (CV) | Object Detection | FaceDetection | ✅ | ||
Computer Vision (CV) | Image Classification | FaceQualityAssessment | ✅ | ||
Computer Vision (CV) | Image Classification | FaceRecognition | ✅ | ||
Computer Vision (CV) | Image Classification | FaceRecognitionForTracking | ✅ | ||
Computer Vision (CV) | Object Detection | Spnas | ✅ | ||
Computer Vision (CV) | Object Detection | SSD-GhostNet | ✅ | ||
Computer Vision (CV) | Key Point Detection | CenterNet | ✅ | ✅ | |
Computer Vision (CV) | Image Style Transfer | CycleGAN | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | DS-CNN | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | TextRCNN | ✅ | ||
Natural Language Processing (NLP) | Natural Language Understanding | TPRR | ✅ | ||
Recommender | Recommender System, CTR prediction | AutoDis | ✅ | ||
Audio | Audio Tagging | FCN-4 | ✅ | ||
High Performance Computing | Molecular Dynamics | DeepPotentialH2O | ✅ | ||
High Performance Computing | Ocean Model | GOMO | ✅ |
models
models
comes from the directory model_zoo
of repository mindspore. This new repository doesn't contain any history of commits about the directory model_zoo
in mindspore
, you could refer to the repository mindspore
for the past commits.
Here is the ModelZoo for MindSpore which support different devices including Ascend, GPU, CPU and mobile.
If you are looking for exclusive models only for Ascend using different ML platform, you could refer to Ascend ModelZoo and corresponding gitee repository
Mindspore only provides scripts that downloads and preprocesses public datasets. We do not own these datasets and are not responsible for their quality or maintenance. Please make sure you have permission to use the dataset under the dataset’s license. The models trained on these dataset are for non-commercial research and educational purpose only.
To dataset owners: we will remove or update all public content upon request if you don’t want your dataset included on Mindspore, or wish to update it in any way. Please contact us through a Github/Gitee issue. Your understanding and contribution to this community is greatly appreciated.
MindSpore is Apache 2.0 licensed. Please see the LICENSE file.
Q: How to resolve the lack of memory while using PYNATIVE_MODE
with errors such as Failed to alloc memory pool memory?
A: PYNATIVE_MODE
usually requires more memory than GRAPH_MODE
, especially in training process which have to deal with back propagation. You could try using smaller batch size.
Q: How to resolve the error about the interface not supported, such as cann not import
?
A: Please check the version of MindSpore and the branch you fetch the modelzoo scripts. Some model scripits in latest branch will use new interface in the latest version of MindSpore.
双通路图卷积神经网络(DGCN)于2018年提出,它基于半监督学习的两个基本假设:(1).局部一致性:距离比较近的数据,通常有相同的标签;(2).全局一致性:处在相似的上下文中的数据,通常有相同的标签,使用了双通路分别嵌入半监督学习局部一致性与全局一致性的信息,并设计了新的loss函数将二者结合,来对图结构数据进行半监督学习,取得了很好的实验效果。
Python Text Shell C++ Markdown other
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