一、ConvNeXt

我们为 ConvNeXt 模型提供实现和预训练权重。

Paper: A ConvNet for the 2020s.

https://arxiv.org/abs/2201.03545
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原始 pytorch 代码和权重来自：

https://github.com/facebookresearch/ConvNeXt
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此代码已从 timm 实现移植。有以下型号可供选择。

1.1 型号

1.1.1 在 ImageNet-1k 上训练的模型

convnext_tiny
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convnext_small
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convnext_base
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convnext_large
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1.1.2 在 ImageNet-22k 上训练的模型，在 ImageNet-1k 上微调

convnext_tiny_in22ft1k
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convnext_small_in22ft1k
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convnext_base_in22ft1k
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convnext_large_in22ft1k
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convnext_xlarge_in22ft1k
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1.1.3 在 ImageNet-22k 上训练的模型，在 ImageNet-1k 上以 384 分辨率进行微调

convnext_tiny_384_in22ft1k
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convnext_small_384_in22ft1k
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convnext_base_384_in22ft1k
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convnext_large_384_in22ft1k
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convnext_xlarge_384_in22ft1k
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1.1.4 在 ImageNet-22k 上训练的模型

convnext_tiny_in22k
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convnext_small_in22k
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convnext_base_in22k
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convnext_large_in22k
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convnext_xlarge_in22k
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1.2 ConvNeXtConfig

classConvNeXtConfig(name='', url='', nb_classes=1000, in_channels=3, input_size=(224, 224), patch_size=4, embed_dim=(96, 192, 384, 768), nb_blocks=(3, 3, 9, 3), mlp_ratio=4.0, conv_mlp_block=False, drop_rate=0.0, drop_path_rate=0.1, norm_layer='layer_norm_eps_1e-6', act_layer='gelu', init_scale=1e-06, crop_pct=0.875, interpolation='bicubic', mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), first_conv='stem/0', classifier='head/fc')
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Parameters:
name (str) – Name of the model.

url (str) – URL for pretrained weights.

nb_classes (int) – Number of classes for classification head.

in_channels (int) – Number of input image channels.

input_size (Tuple[int, int]) – Input image size (height, width)

patch_size (int) – Patchifying the image is implemented via a convolutional layer with kernel size and stride equal to patch_size.

embed_dim (Tuple) – Feature dimensions at each stage.

nb_blocks (Tuple) – Number of blocks at each stage.

mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim

conv_mlp_block (bool) – There are two equivalent implementations of the ConvNeXt block, using either (1) 1x1 convolutions or (2) fully connected layers. In PyTorch option (2) also requires permuting channels, which is not needed in TensorFlow. We offer both implementations here, because some timm models use (1) while others use (2).

drop_rate (float) – Dropout rate.

drop_path_rate (float) – Dropout rate for stochastic depth.

norm_layer (str) – Normalization layer. See norm_layer_factory() for possible values.

act_layer (str) – Activation function. See act_layer_factory() for possible values.

init_scale (float) – Inital value for layer scale weights.

crop_pct (float) – Crop percentage for ImageNet evaluation.

interpolation (str) – Interpolation method for ImageNet evaluation.

mean (Tuple[float, float, float]) – Defines preprocessing function. If x is an image with pixel values in (0, 1), the preprocessing function is (x - mean) / std.

std (Tuple[float, float, float]) – Defines preprpocessing function.

first_conv (str) – Name of first convolutional layer. Used by create_model() to adapt the number in input channels when loading pretrained weights.

classifier (str) – Name of classifier layer. Used by create_model() to adapt the classifier when loading pretrained weights.
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1.3 ConvNeXt

classConvNeXt(*args, **kwargs)
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Parameters:
cfg (ConvNeXtConfig) – Configuration class for the model.

**kwargs – Arguments are passed to tf.keras.Model.
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call(x, training=False, return_features=False)
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Parameters:
x – Input to model

training (bool) – Training or inference phase?

return_features (bool) – If True, we return not only the model output, but a dictionary with intermediate features.
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Returns:
If return_features=True, we return a tuple (y, features), where y is the model output and features is a dictionary with intermediate features.

If return_features=False, we return only y.
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propertydummy_inputs: Tensor
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Returns a tensor of the correct shape for inference.

propertyfeature_names: List[str]
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Names of features, returned when calling call with return_features=True.

forward_features(x, training=False, return_features=False)
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Forward pass through model, excluding the classifier layer. This function is useful if the model is used as input for downstream tasks such as object detection.

Parameters:
x – Input to model

training (bool) – Training or inference phase?

return_features (bool) – If True, we return not only the model output, but a dictionary with intermediate features.
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Returns:
If return_features=True, we return a tuple (y, features), where y is the model output and features is a dictionary with intermediate features.

If return_features=False, we return only y.
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