API - Pretrained Models¶
TensorLayerX provides many pretrained models, you can easily use the whole or a part of the pretrained models via these APIs.
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Pre-trained VGG16 model. |
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Pre-trained VGG19 model. |
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Pre-trained YOLOv4 model. |
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Pre-trained ResNet50 model. |
vgg16¶
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examples.model_zoo.
vgg16
(pretrained=False, end_with='outputs', mode='dynamic', name=None)[source]¶ Pre-trained VGG16 model.
- Parameters
pretrained (boolean) – Whether to load pretrained weights. Default False.
end_with (str) – The end point of the model. Default
fc3_relu
i.e. the whole model.mode (str.) – Model building mode, ‘dynamic’ or ‘static’. Default ‘dynamic’.
name (None or str) – A unique layer name.
Examples
Classify ImageNet classes with VGG16, see tutorial_models_vgg.py With TensorLayer TODO Modify the usage example according to the model storage location
>>> # get the whole model, without pre-trained VGG parameters >>> vgg = vgg16() >>> # get the whole model, restore pre-trained VGG parameters >>> vgg = vgg16(pretrained=True) >>> # use for inferencing >>> output = vgg(img) >>> probs = tlx.softmax(output)[0].numpy()
vgg19¶
-
examples.model_zoo.
vgg19
(pretrained=False, end_with='outputs', mode='dynamic', name=None)[source]¶ Pre-trained VGG19 model.
- Parameters
pretrained (boolean) – Whether to load pretrained weights. Default False.
end_with (str) – The end point of the model. Default
fc3_relu
i.e. the whole model.mode (str.) – Model building mode, ‘dynamic’ or ‘static’. Default ‘dynamic’.
name (None or str) – A unique layer name.
Examples
Classify ImageNet classes with VGG19, see tutorial_models_vgg.py With TensorLayer
>>> # get the whole model, without pre-trained VGG parameters >>> vgg = vgg19() >>> # get the whole model, restore pre-trained VGG parameters >>> vgg = vgg19(pretrained=True) >>> # use for inferencing >>> output = vgg(img) >>> probs = tlx.softmax(output)[0].numpy()
YOLOv4¶
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examples.model_zoo.
YOLOv4
(NUM_CLASS, pretrained=False)[source]¶ Pre-trained YOLOv4 model.
- Parameters
NUM_CLASS (int) – Number of classes in final prediction.
pretrained (boolean) – Whether to load pretrained weights. Default False.
Examples
Object Detection with YOLOv4, see computer_vision.py With TensorLayer
>>> # get the whole model, without pre-trained YOLOv4 parameters >>> yolov4 = YOLOv4(NUM_CLASS=80, pretrained=False) >>> # get the whole model, restore pre-trained YOLOv4 parameters >>> yolov4 = YOLOv4(NUM_CLASS=80, pretrained=True) >>> # use for inferencing >>> output = yolov4(img)
ResNet50¶
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examples.model_zoo.
ResNet50
(pretrained=False, end_with='fc1000', n_classes=1000)[source]¶ Pre-trained ResNet50 model. Input shape [?, 224, 224, 3].
To use pretrained model, input should be in BGR format and subtracted from ImageNet mean [103.939, 116.779, 123.68].
- Parameters
pretrained (boolean) – Whether to load pretrained weights. Default False.
end_with (str) – The end point of the model [conv, depth1, depth2 … depth13, globalmeanpool, out]. Default
out
i.e. the whole model.n_classes (int) – Number of classes in final prediction.
name (None or str) – Name for this model.
Examples
Classify ImageNet classes, see tutorial_models_resnet50.py TODO Modify the usage example according to the model storage location
>>> # get the whole model with pretrained weights >>> resnet = ResNet50(pretrained=True) >>> # use for inferencing >>> output = resnet(img1) >>> prob = tlx.softmax(output)[0].numpy()
Extract the features before fc layer
>>> resnet = ResNet50(pretrained=True, end_with='5c') >>> output = resnet(img1)
- Returns
- Return type
ResNet50 model.