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Zhouyi Model Zoo

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 软件概览

“周易”模型库提供了一组基于周易SDK的AI模型供参考。

Summary Framework Input Shape Model Source
age_googlenet onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/body_analysis/age_gender
alexnet caffe [1,3,227,227] https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
alexnet onnx [1,1,28,28] https://github.com/tensorflow/models/tree/archive/research/slim/nets
alexnet tflite(quant) [1,28,28,1] https://github.com/tensorflow/models/tree/archive/research/slim/nets
alexnet tf [1,28,28,1] https://github.com/tensorflow/models/tree/archive/research/slim/nets
arcface onnx [1, 3, 112, 112] https://github.com/onnx/models/tree/master/vision/body_analysis/arcface
bisenet_v2 tf [4, 512, 1024, 3] https://github.com/MaybeShewill-CV/bisenetv2-tensorflow
caffenet caffe [10, 3, 227, 227] https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet
caffenet onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/classification/caffenet
centerface onnx [1, 3, 640, 640] https://github.com/ttanzhiqiang/onnx_tensorrt_project
crnn_quant tflite(quant) [1,64,200,3] https://ai-benchmark.com/download.html
deeplab_v2 onnx [1, 3, 513, 513] https://github.com/kazuto1011/deeplab-pytorch
deeplab_v3 tflite [1,513,513,3] https://github.com/tensorflow/models/tree/archive/research/deeplab
deeplab_v3 onnx [1,3,513,513] https://github.com/tensorflow/models/tree/archive/research/deeplab
deeplab_v3 tf [1,513,513,3] https://github.com/tensorflow/models/tree/archive/research/deeplab
deeplab_v3_plus_quant tflite(quant) [1,1024,1024,3] https://ai-benchmark.com/download.html
deepspeech_v2 onnx [1,385,161,1] https://github.com/tensorflow/models/tree/archive/research/deep_speech
deepspeech_v2 tf [1,385,161,1] https://github.com/tensorflow/models/tree/archive/research/deep_speech
densenet_121 caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
densenet_169 caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
dilation_8 caffe [1, 3, 900, 900] https://github.com/fyu/dilation
dped_quant tflite(quant) [1,1536,2048,3] https://ai-benchmark.com/download.html
dpn_68_extra caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
dpn_92 caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
duc onnx [1, 3, 800, 800] https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/duc
efficientnet_b4_quant tflite(quant) [1,380,380,3] https://ai-benchmark.com/download.html
efficientnet_b5 tf [1, 456, 456, 3] https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
efficientnet_lite onnx [1,224,224,3] https://github.com/onnx/models/tree/master/vision/classification/efficientnet-lite4
enet caffe [1, 3, 512, 1024] https://github.com/TimoSaemann/Enet
erfnet caffe [1,3,512,1024] https://github.com/Yuelong-Yu/ERFNet-Caffe
esrgan_quant tflite(quant) [1,128,128,3] https://ai-benchmark.com/download.html
face_boxes onnx [1,3,1024,1024] https://github.com/zisianw/FaceBoxes.PyTorch
facenet tf [1, 160, 160, 3] https://github.com/davidsandberg/facenet
fast_depth onnx [1,3,224,224] https://github.com/dwofk/fast-depth
faster_rcnn caffe [1, 3, 224, 224] https://github.com/rbgirshick/py-faster-rcnn
fcn caffe [1,3,224,224] https://github.com/shelhamer/fcn.berkeleyvision.org/tree/master/voc-fcn8s-atonce
fcn16s onnx [1, 3, 224, 224] https://github.com/wkentaro/pytorch-fcn/
fcn32s onnx [1, 3, 224, 224] https://github.com/wkentaro/pytorch-fcn/
fcn8s onnx [1, 3, 224, 224] https://github.com/wkentaro/pytorch-fcn/
googlenet caffe [10, 3, 224, 224] https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
googlenet onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/classification/inception_and_googlenet/googlenet
gru_l tf [1, 49, 10] https://github.com/UT2UH/ML-KWS-for-ESP32/tree/master/Pretrained_models/GRU
icnet caffe [1, 3, 1025, 2049] https://github.com/hszhao/ICNet
imdn_quant tflite(quant) [1,1024,1024,3] https://ai-benchmark.com/download.html
inception_resnet_v2 tflite [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
inception_resnet_v2 caffe [1, 3,331,331] https://github.com/soeaver/caffe-model
inception_resnet_v2 tf [1, 299, 299, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
inception_v1 tflite(quant) [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
inception_v1 tf [1, 224, 224, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
inception_v2 tflite(quant) [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
inception_v2 onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/classification/inception_and_googlenet/inception_v2
inception_v2 tf [1, 224, 224, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
inception_v3 caffe [1, 3, 299, 299] https://github.com/soeaver/caffe-model/tree/master/cls
inception_v3 onnx [1, 3, 299, 299] https://github.com/tensorflow/models/tree/archive/research/slim/nets
inception_v3 tflite(quant) [1,299,299,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
inception_v3 tf [1,299,299,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
inception_v3_quant tflite(quant) [1,346,346,3] https://ai-benchmark.com/download.html
inception_v4 caffe [1,3,299,299] https://github.com/soeaver/caffe-model/tree/master/cls
inception_v4 onnx [1,3,299,299] https://github.com/tensorflow/models/tree/archive/research/slim/nets
inception_v4 tflite(quant) [1,299,299,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
inception_v4 tf [1,299,299,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
lightface onnx [1, 3, 240, 320] https://hailo.ai/devzone-model-zoo/face-detection/
lstm_quant tflite(quant) [1,32,500,1] https://ai-benchmark.com/download.html
mask_rcnn tf [1,1024,1024,3] https://github.com/matterport/Mask_RCNN
mixnet tf [1,224,224,3] https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mixnet-l
mnasnet tflite [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
mobilebert_quant tflite(quant) [1,384], [1,384], [1,384] https://ai-benchmark.com/download.html
mobilenet_edgetpu tflite [1,224,224,3] https://github.com/mlcommons/mobile_models/blob/main/v0_7/tflite/mobilenet_edgetpu_224_1.0_uint8.tflite
mobilenet_v1 tflite(quant) [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
mobilenet_v1 caffe [1, 3, 224, 224] https://github.com/shicai/MobileNet-Caffe
mobilenet_v1_224 tf [1, 224, 224, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
mobilenet_v1_ssd tflite [300, 300] https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/2
mobilenet_v2 caffe [1,3,224,224] https://github.com/shicai/MobileNet-Caffe
mobilenet_v2 tflite(quant) [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets/mobilenet
mobilenet_v2 onnx [1,3,224,224] https://github.com/tensorflow/models/tree/archive/research/slim/nets/mobilenet
mobilenet_v2 tf [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets/mobilenet
mobilenet_v2_b8_quant tflite(quant) [8,224,224,3] https://ai-benchmark.com/download.html
mobilenet_v2_quant tflite(quant) [1,224,224,3] https://ai-benchmark.com/download.html
mobilenet_v2_ssd tf [1,300,300,3] https://github.com/tensorflow/models/tree/archive/research/object_detection/models
mobilenet_v2_ssd onnx [1,300,300,3] https://github.com/tensorflow/models/tree/archive/research/object_detection/models
mobilenet_v2_ssd caffe [1,3,300,300] https://github.com/chuanqi305/MobileNet-SSD
mobilenet_v2_ssd_lite tf [1, 300, 300, 3] https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md
mobilenet_v3 tflite [1,224,224,3] https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
mobilenet_v3 tf [1, 224, 224, 3] https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
mobilenet_v3_b4_quant tflite(quant) [4,512,512,3] https://ai-benchmark.com/download.html
mobilenet_v3_quant tflite(quant) [1,512,512,3] https://ai-benchmark.com/download.html
mtcnn_o caffe [1,3,48,48] https://github.com/CongWeilin/mtcnn-caffe
mtcnn_p caffe [1,3,12,12] https://github.com/CongWeilin/mtcnn-caffe
mtcnn_r caffe [1,3,24,24] https://github.com/CongWeilin/mtcnn-caffe
mv3_depth_quant tflite(quant) [1,1024,1536,3] https://ai-benchmark.com/download.html
nasnet_large tf [1, 331, 331,3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
nasnet_mobile tflite [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
nasnet_mobile tf [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
peleenet caffe [1,3,224,224] https://github.com/Robert-JunWang/PeleeNet/tree/master/caffe
pointnet onnx [32, 9, 4096] https://github.com/yanx27/Pointnet_Pointnet2_pytorch
poly_lanenet onnx [1, 3, 640, 640] https://hailo.ai/devzone-model-zoo/driving-lane-detection/
pspnet onnx [1, 3, 1024, 2048] https://github.com/open-mmlab/mmsegmentation/tree/master/configs/pspnet
punet_quant tflite(quant) [1,544,960,4] https://ai-benchmark.com/download.html
pynet_quant tflite(quant) [1,512,512,3] https://ai-benchmark.com/download.html
regnet_x onnx [1, 3, 224, 224] https://hailo.ai/devzone-model-zoo/about-object-detection/
resnet_34_ssd tf [1, 1200, 1200, 3] https://github.com/mlcommons/inference/tree/r0.5/v0.5/classification_and_detection
resnet_v1_101 tflite(quant) [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
resnet_v1_101 caffe [1,3,224,224] https://github.com/SnailTyan/caffe-model-zoo
resnet_v1_101 onnx [1,3,224,224] https://github.com/tensorflow/models/tree/archive/research/slim/nets
resnet_v1_101 tf [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
resnet_v1_152 tf [1, 224, 224, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
resnet_v1_50 tflite(quant) [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
resnet_v1_50 onnx [1,3,224,224] https://github.com/tensorflow/models/tree/archive/research/slim/nets
resnet_v1_50 caffe [1,3,224,224] https://github.com/SnailTyan/caffe-model-zoo
resnet_v1_50 tf [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
resnet_v2_101 tf [1, 299, 299, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
resnet_v2_101 caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
resnet_v2_101 tflite [1, 224, 224, 3] https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn
resnet_v2_152 tf [1, 299, 299, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
resnet_v2_152 caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
resnet_v2_50 tf [1, 299, 299, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
resnext_101 onnx [1, 3, 224, 224] https://github.com/Cadene/pretrained-models.pytorch
resnext_101 caffe [1, 3, 224, 224] https://www.deepdetect.com/models/resnext/
resnext_50 caffe [1, 3, 224, 224] https://github.com/soeaver/caffe-model
rnn_t_decoder onnx [1, 1],[1, 2, 320],[1, 2, 320],[1, 1, 1024] https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt
rnn_t_encoder onnx [1,249,240] https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt
se_inception caffe [1, 3, 224, 224] https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/se-inception/README.md
se_resnet_101 caffe [1, 3, 224, 224] https://github.com/hujie-frank/SENet
se_resnet_50 tf [1, 224, 224, 3] https://github.com/HiKapok/TF-SENet
shufflenet_v1 onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/classification/shufflenet
shufflenet_v2 caffe [1,3,224,224] https://github.com/Ewenwan/ShuffleNet-2
shufflenet_v2 tflite(quant) [1,224,224,3] https://github.com/TropComplique/shufflenet-v2-tensorflow
shufflenet_v2 onnx [1,3,224,224] https://github.com/TropComplique/shufflenet-v2-tensorflow
shufflenet_v2 tf [1,224,224,3] https://github.com/TropComplique/shufflenet-v2-tensorflow
sne_roadseg onnx [1,3,384,1248] https://github.com/hlwang1124/SNE-RoadSeg
squeezenet caffe [10, 3, 227, 227] https://github.com/forresti/SqueezeNet
squeezenet_v1 onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/classification/squeezenet
srcnn tf [1, 33, 33, 1] https://github.com/tegg89/SRCNN-Tensorflow
srgan_quant tflite(quant) [1,256,256,3] https://ai-benchmark.com/download.html
stacked_hourglass tf [1, 256, 256, 3] https://github.com/yuanyuanli85/Stacked_Hourglass_Network_Keras
super_resolution onnx [1, 1, 224, 224] https://github.com/onnx/models/tree/master/vision/super_resolution/sub_pixel_cnn_2016
unet_bio tf [1,256, 256,1] https://github.com/zhixuhao/unet
unet_quant tflite(quant) [1,1024,1024,3] https://ai-benchmark.com/download.html
vgg_16 onnx [1,3,224,224] https://github.com/tensorflow/models/tree/archive/research/slim/nets
vgg_16 caffe [1,3,224,224] https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
vgg_16 tflite(quant) [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
vgg_16 tf [1,224,224,3] https://github.com/tensorflow/models/tree/archive/research/slim/nets
vgg_19 tf [1, 224, 224, 3] https://github.com/tensorflow/models/tree/master/research/slim#Pretrained
vgg_cnn_s caffe [10, 3, 224, 224] https://gist.github.com/ksimonyan/fd8800eeb36e276cd6f9
vgg_quant tflite(quant) [1,256,256,1] https://ai-benchmark.com/download.html
vgg_ssd caffe [1, 3, 300, 300] https://github.com/weiliu89/caffe/tree/ssd
vsr_quant tflite(quant) [1,540,960,3] https://ai-benchmark.com/download.html
wavenet onnx [1,390,23] https://github.com/buriburisuri/speech-to-text-wavenet
wavenet tf [1,390,23] https://github.com/buriburisuri/speech-to-text-wavenet
xception caffe [1, 3, 299, 299] https://github.com/soeaver/caffe-model
xlsr_quant tflite(quant) [1,360,640,3] https://ai-benchmark.com/download.html
yolact_regnetx onnx [1, 3, 512, 512] https://hailo.ai/devzone-model-zoo/instance-segmentation/
yolo_v2_416 caffe [1,3,416,416] https://github.com/tsingjinyun/caffe-yolov2
yolo_v2_416 tf [1,416,416,3] https://github.com/wojciechmo/yolo2
yolo_v2_416 onnx [1,3,416,416] https://github.com/wojciechmo/yolo2
yolo_v3 tf [1, 416, 416, 3] https://github.com/qqwweee/keras-yolo3
yolo_v3 caffe [1, 3, 608, 608] https://github.com/foss-for-synopsys-dwc-arc-processors/synopsys-caffe-models/tree/master/caffe_models/yolo_v3
yolo_v4 onnx [1, 416, 416, 3] https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov4
yolo_v4_tiny_quant tflite(quant) [1,416,416,3] https://ai-benchmark.com/download.html
zfnet_512 onnx [1, 3, 224, 224] https://github.com/onnx/models/tree/master/vision/classification/zfnet-512

FTP 模型下载(建议使用FTP下载工具 - FileZilla)

  • 地址: sftp://sftp01.armchina.com
  • 帐号: zhouyi.armchina
  • 密码: 114r3cJd
 使用案例
  • 深度学习框架提供的“Model Zoo

    深度学习框架提供的 “Model Zoo” ，有大量的在大数据集上预训练的可供下载的模型: Caffe: https://github.com/BVLC/caffe/wiki/Model-Zoo TensorFlow: https://github.com/tensorflow/models PyTorch: https://github.com/pytorch/vision

  • mmdetection相关model zoo汇总

    镜像地址 从 MMDetection V2.0 起，我们只通过阿里云维护模型库。V1.x 版本的模型已经弃用。 共同设置 所有模型都是在 coco_2017_train 上训练，在 coco_2017_val 上测试。 我们使用分布式训练。 所有 pytorch-style 的 ImageNet 预训练主干网络来自 PyTorch 的模型库，caffe-style 的预训练主干网络来自 detec

  • Detectron Model Zoo and Baselines

    参考 Detectron Model Zoo and Baselines - 云+社区 - 腾讯云 Introduction This file documents a large collection of baselines trained with Detectron, primarily in late December 2017. We refer to these results as

  • Caffe Model Zoo

    Caffe有许多分类的预训练模型及网络结构，我自己训练过的模型总结在Github上，基本上涵盖了大部分的分类模型，包括AlexNet，VGG，GoogLeNet，Inception系列，ResNet，SENet，DenseNet，SqueezeNet。 其中会碰到不少坑，例如VGG给的结构已经太旧了，需要根据新版本Caffe的进行修改，DenseNet训练网络结构有些地方需要修改等。鉴于以上原因，

  • model_zoo里面有各种预训练模型

    举例：Faster-RCNN基于vgg19提取features，但是只使用了vgg19一部分模型提取features。 步骤： 下载vgg19的pth文件，在anaconda中直接设置pretrained=True下载一般都比较慢，在model_zoo里面有各种预训练模型的下载链接： model_urls = { ‘vgg11‘: ‘https://download.pytorch.org/mod

  • OpenCV - Open Model Zoo

    OpenCV - Open Model Zoo https://github.com/opencv/open_model_zoo pre-trained deep learning models and samples Deep Learning For Computer Vision https://software.intel.com/en-us/openvino-toolkit/deep-l

  • pytorch utils.model_zoo

    应用 API torch.utils.model_zoo.load_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None) 加载序列化的torch对象。 如果对象已经存在model_dir，则直接加载。model_dir在<hub_dir>/checkpoints中，

  • 9、TORCH.UTILS.MODEL_ZOO

    torch.utils.model_zoo.load_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None) 在给定的 URL 加载 Torch 序列化对象。 如果下载的文件是 zip 文件，它会被自动解压。 如果该对象已存在于 model_dir 中，则将其反序列化并

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