Model Zoo for MindSpore

Related tags

Deep Learning models
Overview

MindSpore Logo

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
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 ✅

Research

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 ✅

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: 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.
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Comments
  • RCAN

    RCAN

    What type of PR is this?

    Uncomment only one /kind <> line, hit enter to put that in a new line, and remove leading whitespaces from that line:

    /kind bug
    /kind task
    /kind feature

    What does this PR do / why do we need it:

    Which issue(s) this PR fixes:

    Fixes #

    Special notes for your reviewers:

    opened by yangyucheng000 2
  • RuntimeError: For 'Reshape', the size of 'input_x': {3456} is not equal to the size of the first output: {5760}

    RuntimeError: For 'Reshape', the size of 'input_x': {3456} is not equal to the size of the first output: {5760}

    I use the dataset you provided,but I can't train.How to solve this problem?

    root@0563a279aa9b:/data# DEVICE_ID=0 python train.py Start time : 2022-09-22 08:07:09

    infos : {'dataset_path': './dataset/', 'backbone_pretrained': './src/model/res2net_pretrained.ckpt', 'dataset_train': 'PASCAL_SBD', 'datasets_val': ['GrabCut', 'Berkeley'], 'epochs': 33, 'train_only_epochs': 32, 'val_robot_interval': 1, 'lr': 0.007, 'batch_size': 8, 'max_num': 0, 'size': (384, 384), 'device': 'CPU', 'num_workers': 4, 'itis_pro': 0.7, 'max_point_num': 20, 'record_point_num': 5, 'pred_tsh': 0.5, 'miou_target': [0.9, 0.9], 'resume': None, 'snapshot_path': './snapshot'}

    Traceback (most recent call last): File "train.py", line 35, in mine = Trainer(p) File "/data/src/trainer.py", line 111, in init size=p["size"][0], backbone_pretrained=p["backbone_pretrained"] File "/data/src/model/fcanet.py", line 295, in init resnet.load_pretrained_model(backbone_pretrained) File "/data/src/model/res2net.py", line 267, in load_pretrained_model tmp[:, :3, :, :] = parameter_dict["conv1_0.weight"] File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/common/tensor.py", line 344, in setitem out = tensor_operator_registry.get('setitem')(self, index, value) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/_compile_utils.py", line 67, in _tensor_setitem return tensor_setitem_by_tuple(self, index, value) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/_compile_utils.py", line 803, in tensor_setitem_by_tuple return tensor_setitem_by_tuple_with_tensor(self, index, value) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/_compile_utils.py", line 956, in tensor_setitem_by_tuple_with_tensor tuple_index, value, idx_advanced = remove_expanded_dims(tuple_index, F.shape(data), value) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/compile_utils.py", line 1156, in remove_expanded_dims value = F.reshape(value, value_shape) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/function/array_func.py", line 857, in reshape return reshape(input_x, input_shape) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/primitive.py", line 294, in call return _run_op(self, self.name, args) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/common/api.py", line 98, in wrapper results = fn(*arg, **kwargs) File "/usr/local/python-3.7.5/lib/python3.7/site-packages/mindspore/ops/primitive.py", line 748, in _run_op output = real_run_op(obj, op_name, args) RuntimeError: For 'Reshape', the size of 'input_x': {3456} is not equal to the size of the first output: {5760}

    • C++ Call Stack: (For framework developers)

    mindspore/ccsrc/plugin/device/cpu/kernel/memcpy_cpu_kernel.cc:37 Launch

    opened by lulianLiu 0
  • Run-Time and Memory Measurement

    Run-Time and Memory Measurement

    (Regarding eppmvsnet) Hi, I am trying to measure the runtime and memory usage of a set of methods as your table 3 in the paper shows, but didn't get the same numbers. Could you provide more details regarding how you measure them? Thanks!

    opened by mazeyu 0
  • How to load the model's parameters during prediction after the data and optimizator parallelism at the training time? 【pangu-alpha采用数据并行+优化器并行方式训练，predict时候如何加载参数。】

    How to load the model's parameters during prediction after the data and optimizator parallelism at the training time? 【pangu-alpha采用数据并行+优化器并行方式训练，predict时候如何加载参数。】

    Task Description

    How to load pangu-alpha model's parameters during prediction after the data and optimizator parallelism at the training time?
    【pangu-alpha采用数据并行+优化器并行方式训练，predict时候如何加载参数。】

    Task Goal

    In the MindSpore tutorial and also the course, there are several instructions about how to use the distributed model do to the training and prediction(model loading). but those instructions only include the data parallelism and automatic parallelism. Following those instructions, there is only one generated checkpoint file, and that is straightforward how to load the model during prediction. However, I cannot find out any instruction to introduce how to load the mode if I trained my model with data parallelism and optimizator parallelism. In this case, each card will generate a checkpoint file, and I am not sure which one should be loaded during prediction. For example, I use 64 cards to train my model, and wanna use 1 card or 8 cards to predict. In this case, there are multiple checkpoint files, which one should I select to use?

    【在MindSpore的教程中，关于分布式并行模型的训练和加载，只介绍了数据并行和自动并行两种情况，这两种情况保存的参数只有一个checkpoint文件，加载方法比较简单。然而，在其他的一些情况，MindSpore的教程及Readme中，没有说明如何处理。比如，在使用“数据并行”+“优化器并行”，每张卡的checkpoint是不一样的，不知道具体加载那个checkpoint。比如使用64卡训练，想单卡推理或者8卡推理加载，该如何操作？】

    opened by xupeng1910 0
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Mirror repo for gitee.com/mindspore, PR and ISSUE Open
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