• MindSpore Logo
• • Welcome to the Model Zoo for MindSpore
  • Table of Contents
  • • Official
    • Research
  • Announcements
  • • 2021.9.15 Set up repository models
  • Related Website
  • Disclaimers
  • License
  • FAQ

Welcome to the Model Zoo for MindSpore

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

Table of Contents

Official

Domain	Sub Domain	Network	Ascend	GPU	CPU
Audio	Speech Synthesis	LPCNet	✅
Audio	Speech Synthesis	MelGAN	✅
Audio	Speech Synthesis	Tacotron2	✅
Computer Vision (CV)	Point Cloud Model	OctSqueeze	✅
Computer Vision (CV)	Optical Flow Estimation	PWCNet	✅
Computer Vision (CV)	Object Tracking	Deepsort	✅
Computer Vision (CV)	Object Tracking	ADNet	✅
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	MobileNetV1	✅
Computer Vision (CV)	Image Classification	MobileNetV2	✅	✅	✅
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-34	✅
Computer Vision (CV)	Image Classification	ResNet-50	✅	✅	✅
Computer Vision (CV)	Image Classification	ResNet-101	✅	✅
Computer Vision (CV)	Image Classification	ResNet-152	✅
Computer Vision (CV)	Image Classification	ResNeXt50	✅	✅
Computer Vision (CV)	Image Classification	ResNeXt101	✅
Computer Vision (CV)	Image Classification	SE-ResNet50	✅
Computer Vision（CV）	Image Classification	SE-ResNext50	✅
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)	Image Classification	CspDarkNet53	✅
Computer Vision (CV)	Image Classification	ErfNet	✅
Computer Vision (CV)	Image Classification	SimCLR	✅
Computer Vision (CV)	Image Classification	Vit	✅
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	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	YOLOv4	✅
Computer Vision (CV)	Object Detection	YOLOv5	✅
Computer Vision (CV)	Object Detection	RetinaNet	✅
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	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)	Semantic Segmentation	Fast-SCNN	✅
Computer Vision (CV)	Semantic Segmentation	FCN8s	✅
Computer Vision (CV)	6DoF Pose Estimation	PVNet	✅
Computer Vision (CV)	Keypoint Detection	OpenPose	✅
Computer Vision (CV)	Keypoint Detection	SimplePoseNet	✅
Computer Vision (CV)	Scene Text Detection	East	✅
Computer Vision (CV)	Scene Text Detection	PSENet	✅
Computer Vision (CV)	Scene Text Recognition	CRNN	✅
Computer Vision (CV)	Scene Text Recognition	CNN+CTC	✅
Computer Vision (CV)	Scene Text Recognition	CRNN-Seq2Seq-OCR	✅
Computer Vision (CV)	Scene Text Recognition	WarpCTC	✅
Computer Vision (CV)	Defect Detection	ssim-ae	✅
Computer Vision (CV)	Defect Detection	PatchCore	✅
Computer Vision (CV)	Face Detection	RetinaFace-ResNet50	✅	✅
Computer Vision (CV)	Face Detection	CenterFace	✅
Computer Vision (CV)	Face Detection	SphereFace	✅
Computer Vision (CV)	Crowd Counting	MCNN	✅
Computer Vision (CV)	Depth Estimation	DepthNet	✅
Computer Vision (CV)	Camera Relocalization	PoseNet	✅
Computer Vision (CV)	Image Matting	Semantic Human Matting	✅
Computer Vision (CV)	Video Classification	C3D	✅
Computer Vision (CV)	Image Super-Resolution	SRCNN	✅
Computer Vision (CV)	Image Super-Resolution	RDN	✅	✅
Computer Vision (CV)	Image Denoising	BRDNet	✅
Computer Vision (CV)	Image Denoising	DnCNN	✅
Computer Vision (CV)	Image Denoising	Learning-to-See-in-the-Dark	✅
Computer Vision (CV)	Image Quality Assessment	NIMA	✅
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	✅
Natural Language Processing (NLP)	Natural Language Understanding	CPM	✅
Natural Language Processing (NLP)	Natural Language Understanding	ERNIE	✅
Natural Language Processing (NLP)	Natural Language Understanding	GPT-3	✅
Natural Language Processing (NLP)	Emotion Classification	EmoTect	✅
Natural Language Processing (NLP)	Emotion Classification	LSTM	✅
Natural Language Processing (NLP)	Dialogue Generation	DGU	✅
Natural Language Processing (NLP)	Dialogue Generation	DuConv	✅
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	✅

Research

Domain	Sub Domain	Network	Ascend	GPU	CPU
Computer Vision (CV)	Image Classification	3D Densenet	✅
Computer Vision (CV)	Image Classification	Auto Augment	✅
Computer Vision (CV)	Image Classification	AVA	✅
Computer Vision (CV)	Image Classification	CCT	✅
Computer Vision (CV)	Image Classification	dnet-nas	✅
Computer Vision (CV)	Image Classification	Efficientnet-b0	✅
Computer Vision (CV)	Image Classification	Efficientnet-b1	✅
Computer Vision (CV)	Image Classification	Efficientnet-b2	✅
Computer Vision (CV)	Image Classification	Efficientnet-b3	✅
Computer Vision (CV)	Image Classification	FDA-BNN	✅
Computer Vision (CV)	Image Classification	fishnet99	✅
Computer Vision (CV)	Image Classification	GENET	✅
Computer Vision (CV)	Image Classification	GhostNet	✅
Computer Vision (CV)	Image Classification	Glore_res200	✅
Computer Vision (CV)	Image Classification	Glore_res50	✅
Computer Vision (CV)	Image Classification	HarDNet	✅
Computer Vision (CV)	Image Classification	HourNAS	✅
Computer Vision (CV)	Image Classification	HRNetW48-cls	✅
Computer Vision (CV)	Image Classification	ibn-net	✅
Computer Vision (CV)	Image Classification	Inception ResNet V2	✅
Computer Vision (CV)	Image Classification	Resnetv2_50_frn	✅
Computer Vision (CV)	Image Classification	META-Baseline	✅
Computer Vision (CV)	Image Classification	MNasNet	✅
Computer Vision (CV)	Image Classification	MobilenetV3-Large	✅
Computer Vision (CV)	Image Classification	MobilenetV3-Small	✅
Computer Vision (CV)	Image Classification	NFNet-F0	✅
Computer Vision (CV)	Image Classification	ntsnet	✅
Computer Vision (CV)	Image Classification	Pdarts	✅
Computer Vision (CV)	Image Classification	PNASNet-5	✅
Computer Vision (CV)	Image Classification	ProtoNet	✅
Computer Vision (CV)	Image Classification	Proxylessnas	✅
Computer Vision (CV)	Image Classification	RelationNet	✅
Computer Vision (CV)	Image Classification	renas	✅
Computer Vision (CV)	Image Classification	Res2net	✅
Computer Vision (CV)	Image Classification	ResNeSt-50	✅
Computer Vision (CV)	Image Classification	ResNet50-BAM	✅
Computer Vision (CV)	Image Classification	ResNet50-quadruplet	✅
Computer Vision (CV)	Image Classification	ResNet50-triplet	✅
Computer Vision (CV)	Image Classification	ResNetV2	✅
Computer Vision (CV)	Image Classification	ResNeXt152_vd_64x4d	✅
Computer Vision (CV)	Image Classification	SE-Net	✅
Computer Vision (CV)	Image Classification	SERes2Net50	✅
Computer Vision (CV)	Image Classification	SinglePathNas	✅
Computer Vision (CV)	Image Classification	SKNet-50	✅
Computer Vision (CV)	Image Classification	SPPNet	✅
Computer Vision (CV)	Image Classification	SqueezeNet	✅
Computer Vision (CV)	Image Classification	SqueezeNet1_1	✅
Computer Vision (CV)	Image Classification	Swin Transformer	✅
Computer Vision (CV)	Image Classification	TNT	✅
Computer Vision (CV)	Image Classification	VGG19	✅
Computer Vision (CV)	Image Classification	Vit-Base	✅
Computer Vision (CV)	Image Classification	Wide ResNet	✅
Computer Vision (CV)	Image Classification	FaceAttributes	✅
Computer Vision (CV)	Image Classification	FaceQualityAssessment	✅
Computer Vision (CV)	Re-Identification	Aligned-ReID	✅
Computer Vision (CV)	Re-Identification	DDAG	✅
Computer Vision (CV)	Re-Identification	MVD	✅
Computer Vision (CV)	Re-Identification	OSNet	✅
Computer Vision (CV)	Re-Identification	PAMTRI	✅
Computer Vision (CV)	Re-Identification	VehicleNet	✅
Computer Vision (CV)	Face Detection	FaceDetection	✅
Computer Vision (CV)	Face Detection	FaceBoxes	✅
Computer Vision (CV)	Face Detection	RetinaFace	✅
Computer Vision (CV)	Face Recognition	Arcface	✅
Computer Vision (CV)	Face Recognition	DeepID	✅
Computer Vision (CV)	Face Recognition	FaceRecognition	✅
Computer Vision (CV)	Face Recognition	FaceRecognitionForTracking	✅
Computer Vision (CV)	Face Recognition	LightCNN	✅
Computer Vision (CV)	Object Detection	Spnas	✅
Computer Vision (CV)	Object Detection	SSD-GhostNet	✅
Computer Vision (CV）	Object Detection	EGNet	✅
Computer Vision (CV)	Object Detection	FasterRCNN-FPN-DCN	✅
Computer Vision (CV)	Object Detection	NAS-FPN	✅
Computer Vision (CV)	Object Detection	RAS	✅
Computer Vision (CV)	Object Detection	r-cnn	✅
Computer Vision (CV)	Object Detection	RefineDet	✅
Computer Vision (CV)	Object Detection	Res2net_fasterrcnn	✅
Computer Vision (CV)	Object Detection	Res2net_yolov3	✅
Computer Vision (CV)	Object Detection	Retinanet_resnet101	✅
Computer Vision (CV)	Object Detection	SSD_MobilenetV2_fpnlite	✅
Computer Vision (CV)	Object Detection	ssd_mobilenet_v2	✅
Computer Vision (CV)	Object Detection	ssd_resnet50	✅
Computer Vision (CV)	Object Detection	ssd_inceptionv2	✅
Computer Vision (CV)	Object Detection	ssd_resnet34	✅
Computer Vision (CV)	Object Detection	U-2-Net	✅
Computer Vision (CV)	Object Detection	YOLOV3-tiny	✅
Computer Vision (CV)	Object Tracking	SiamFC	✅
Computer Vision (CV)	Object Tracking	SiamRPN	✅
Computer Vision (CV)	Object Tracking	FairMOT	✅
Computer Vision (CV)	Key Point Detection	CenterNet	✅		✅
Computer Vision (CV)	Key Point Detection	CenterNet-hourglass	✅
Computer Vision (CV)	Key Point Detection	CenterNet-resnet101	✅
Computer Vision (CV)	Key Point Detection	CenterNet-resnet50	✅
Computer Vision (CV)	Point Cloud Model	PointNet	✅
Computer Vision (CV)	Point Cloud Model	PointNet++	✅
Computer Vision (CV)	Depth Estimation	midas	✅
Computer Vision (CV)	Sequential Image Classification	TCN	✅
Computer Vision (CV)	Temporal Localization	TALL	✅
Computer Vision (CV)	Image Matting	FCA-net	✅
Computer Vision (CV)	Video Classification	Attention Cluster	✅
Computer Vision (CV)	Video Classification	ECO-lite	✅
Computer Vision (CV)	Video Classification	R(2+1)D	✅
Computer Vision (CV)	Video Classification	Resnet-3D	✅
Computer Vision (CV)	Video Classification	StNet	✅
Computer Vision (CV)	Video Classification	TSM	✅
Computer Vision (CV)	Video Classification	TSN	✅
Computer Vision (CV)	Zero-Shot Learnning	DEM	✅
Computer Vision (CV)	Style Transfer	AECRNET	✅
Computer Vision (CV)	Style Transfer	APDrawingGAN	✅
Computer Vision (CV)	Style Transfer	Arbitrary-image-stylization	✅
Computer Vision (CV)	Style Transfer	AttGAN	✅
Computer Vision (CV)	Style Transfer	CycleGAN	✅
Computer Vision (CV)	Image Super-Resolution	CSD	✅
Computer Vision (CV)	Image Super-Resolution	DBPN	✅
Computer Vision (CV)	Image Super-Resolution	EDSR	✅
Computer Vision (CV)	Image Super-Resolution	esr-ea	✅
Computer Vision (CV)	Image Super-Resolution	ESRGAN	✅
Computer Vision (CV)	Image Super-Resolution	IRN	✅
Computer Vision (CV)	Image Super-Resolution	RCAN	✅
Computer Vision (CV)	Image Super-Resolution	sr-ea	✅
Computer Vision (CV)	Image Super-Resolution	SRGAN	✅
Computer Vision (CV)	Image Super-Resolution	wdsr	✅
Computer Vision (CV)	Image Denoising	Neighbor2Neighbor	✅
Computer Vision (CV)	Image Generation	CGAN	✅
Computer Vision (CV)	Image Generation	DCGAN	✅
Computer Vision (CV)	Image Generation	GAN	✅
Computer Vision (CV)	Image Generation	IPT	✅
Computer Vision (CV)	Image Generation	pgan	✅
Computer Vision (CV)	Image Generation	Photo2Cartoon	✅
Computer Vision (CV)	Image Generation	Pix2Pix	✅
Computer Vision (CV)	Image Generation	SinGAN	✅
Computer Vision (CV)	Image Generation	StarGAN	✅
Computer Vision (CV)	Image Generation	STGAN	✅
Computer Vision (CV)	Image Generation	WGAN	✅
Computer Vision (CV)	Scene Text Detection	AdvancedEast	✅
Computer Vision (CV)	Scene Text Detection	TextFuseNet	✅
Computer Vision (CV)	Scene Text Recognition	ManiDP	✅
Computer Vision (CV)	Semantic Segmentation	3d-cnn	✅
Computer Vision (CV)	Semantic Segmentation	adelaide_ea	✅
Computer Vision (CV)	Semantic Segmentation	DDRNet	✅
Computer Vision (CV)	Semantic Segmentation	E-Net	✅
Computer Vision (CV)	Semantic Segmentation	Hrnet	✅
Computer Vision (CV)	Semantic Segmentation	ICNet	✅
Computer Vision (CV)	Semantic Segmentation	PSPnet	✅
Computer Vision (CV)	Semantic Segmentation	RefineNet	✅
Computer Vision (CV)	Semantic Segmentation	Res2net_deeplabv3	✅
Computer Vision (CV)	Semantic Segmentation	UNet 3+	✅
Computer Vision (CV)	Semantic Segmentation	V-net	✅
Computer Vision (CV)	Semantic Segmentation	Autodeeplab	✅
Computer Vision (CV)	Pose Estimation	AlphaPose	✅
Computer Vision (CV)	Pose Estimation	Hourglass	✅
Computer Vision (CV)	Pose Estimation	Simple Baseline	✅
Computer Vision (CV)	Image Retrieval	Delf	✅
Natural Language Processing (NLP)	Word Embedding	Word2Vec Skip-Gram	✅
Natural Language Processing (NLP)	Dialogue Generation	DAM	✅
Natural Language Processing (NLP)	Machine Translation	Seq2Seq	✅
Natural Language Processing (NLP)	Emotion Classification	Senta	✅
Natural Language Processing (NLP)	Emotion Classification	Attention LSTM	✅
Natural Language Processing (NLP)	Named Entity Recognition	LSTM_CRF	✅
Natural Language Processing (NLP)	Text Classification	HyperText	✅
Natural Language Processing (NLP)	Text Classification	TextRCNN	✅
Natural Language Processing (NLP)	Natural Language Understanding	ALBert	✅
Natural Language Processing (NLP)	Natural Language Understanding	KT-Net	✅
Natural Language Processing (NLP)	Natural Language Understanding	LUKE	✅
Natural Language Processing (NLP)	Natural Language Understanding	TPRR	✅
Natural Language Processing (NLP)	Knowledge Graph Embedding	RotatE	✅
Recommender	Recommender System, CTR prediction	AutoDis	✅
Recommender	Recommender System, CTR prediction	DeepFFM	✅
Recommender	Recommender System, CTR prediction	DIEN	✅
Recommender	Recommender System, CTR prediction	DLRM	✅
Recommender	Recommender System, CTR prediction	EDCN	✅
Recommender	Recommender System, CTR prediction	MMOE	✅
Audio	Audio Tagging	FCN-4	✅
Audio	Keyword Spotting	DS-CNN	✅
Audio	Speech Recognition	CTCModel	✅
Audio	Speech Synthesis	Wavenet	✅
GNN	Traffic Prediction	STGCN	✅
GNN	Traffic Prediction	TGCN	✅
GNN	Social and Information Networks	SGCN	✅
GNN	Graph Classification	DGCN	✅
GNN	Graph Classification	SDNE	✅
High Performance Computing	Molecular Dynamics	DeepPotentialH2O	✅
High Performance Computing	Ocean Model	GOMO		✅
Reinforcement Learning	Recommender System, CTR prediction	MMOE	✅

• Community

Announcements

2021.9.15 Set up repository 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.

Related Website

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

If you are looking for some pretrained checkpoint of mindspore, you could refer to MindSpore Hub or Download Website.

Disclaimers

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.

License

Apache License 2.0

FAQ

For more information about MindSpore framework, please refer to FAQ

• Q: How to resolve the lack of memory while using the model directly under "models" with errors such as Failed to alloc memory pool memory?

  A: The typical reason for insufficient memory when directly using models under "models" is due to differences in operating mode (PYNATIVE_MODE), operating environment configuration, and license control (AI-TOKEN).

  • PYNATIVE_MODE usually uses more memory than GRAPH_MODE , especially in the training graph that needs back propagation calculation, there are two ways to try to solve this problem.
    Method 1: You can try to use some smaller batch size;
    Method 2: Add context.set_context(mempool_block_size="XXGB"), where the current maximum effective value of "XX" can be set to "31".
    If method 1 and method 2 are used in combination, the effect will be better.
  • The operating environment will also cause similar problems due to the different configurations of NPU cores, memory, etc.;
  • Different gears of License control (AI-TOKEN ) will cause different memory overhead during execution. You can also try to use some smaller batch sizes.

• Q: How to resolve the error about the interface are not supported in some network operations, 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.

• Q: What is Some RANK_TBAL_FILE which mentioned in many models?

  A: RANK_TABLE_FILE is the config file of cluster on Ascend while running distributed training. For more information, you could refer to the generator hccl_tools and Parallel Distributed Training Example

• Q: How to run the scripts on Windows system?

  A: Most the start-up scripts are written in bash, but we usually can't run bash directly on Windows. You can try start python directly without bash scripts. If you really need the start-up bash scripts, we suggest you the following method to get a bash environment on Windows:

  1. Use a virtual system or docker container with linux system. Then run the scripts in the virtual system or container.
  2. Use WSL, you could turn on the Windows Subsystem for Linux on Windows to obtain an linux system which could run the bash scripts.
  3. Use some bash tools on Windows, such as cygwin and git bash.

• Q: How to resolve the compile error point to gflags when infer on ascend310 with errors such as undefined reference to 'google::FlagRegisterer::FlagRegisterer'?

  A: Please check the version of GCC and gflags. You can refer to GCC and gflags to install GCC and gflags. You need to ensure that the components used are ABI compatible, for more information, please refer to _GLIBCXX_USE_CXX11_ABI.

• Q: How to solve the error when loading dataset in mindrecord format on Mac system, such as Invalid file, failed to open files for reading mindrecord files.?

  A: Please check the system limit with ulimit -a, if the number of file descriptors is 256 (default), you need to use ulimit -n 1024 to set it to 1024 (or larger). Then check whether the file is damaged or modified.

• Q: What should I do if I can't reach the accuracy while training with several servers instead of a single server?

  A: Most of the models has only been trained on single server with at most 8 pcs. Because the batch_size used in MindSpore only represent the batch size of single GPU/NPU, the global_batch_size will increase while training with multi-server. Different gloabl_batch_size requires different hyper parameter including learning_rate, etc. So you have to optimize these hyperparameters will training with multi-servers.