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图像分类器模型

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ImageClassifier class

keras_cv.models.ImageClassifier(
    backbone, num_classes, pooling="avg", activation="softmax", **kwargs
)

Image classifier with pooling and dense layer prediction head.

Arguments

  • backbone: keras.Model instance, the backbone architecture of the classifier called on the inputs. Pooling will be called on the last dimension of the backbone output.
  • num_classes: int, number of classes to predict.
  • pooling: str, type of pooling layer. Must be one of "avg", "max".
  • activation: Optional str or callable, defaults to "softmax". The activation function to use on the Dense layer. Set activation=None to return the output logits.

Example

input_data = tf.ones(shape=(8, 224, 224, 3))

# Pretrained classifier (e.g., for imagenet categories)
model = keras_cv.models.ImageClassifier.from_preset(
    "resnet50_v2_imagenet_classifier",
)
output = model(input_data)

# Pretrained backbone
backbone = keras_cv.models.ResNet50V2Backbone.from_preset(
    "resnet50_v2_imagenet",
)
model = keras_cv.models.ImageClassifier(
    backbone=backbone,
    num_classes=4,
)
output = model(input_data)

# Randomly initialized backbone with a custom config
model = keras_cv.models.ImageClassifier(
    backbone=keras_cv.models.ResNet50V2Backbone(),
    num_classes=4,
)
output = model(input_data)

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from_preset method

ImageClassifier.from_preset()

Instantiate ImageClassifier model from preset config and weights.

Arguments

  • preset: string. Must be one of "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet18_v2", "resnet34_v2", "resnet50_v2", "resnet101_v2", "resnet152_v2", "mobilenet_v3_small", "mobilenet_v3_large", "csp_darknet_tiny", "csp_darknet_s", "csp_darknet_m", "csp_darknet_l", "csp_darknet_xl", "efficientnetv1_b0", "efficientnetv1_b1", "efficientnetv1_b2", "efficientnetv1_b3", "efficientnetv1_b4", "efficientnetv1_b5", "efficientnetv1_b6", "efficientnetv1_b7", "efficientnetv2_s", "efficientnetv2_m", "efficientnetv2_l", "efficientnetv2_b0", "efficientnetv2_b1", "efficientnetv2_b2", "efficientnetv2_b3", "densenet121", "densenet169", "densenet201", "efficientnetlite_b0", "efficientnetlite_b1", "efficientnetlite_b2", "efficientnetlite_b3", "efficientnetlite_b4", "yolo_v8_xs_backbone", "yolo_v8_s_backbone", "yolo_v8_m_backbone", "yolo_v8_l_backbone", "yolo_v8_xl_backbone", "vitdet_base", "vitdet_large", "vitdet_huge", "videoswin_tiny", "videoswin_small", "videoswin_base", "resnet50_imagenet", "resnet50_v2_imagenet", "mobilenet_v3_large_imagenet", "mobilenet_v3_small_imagenet", "csp_darknet_tiny_imagenet", "csp_darknet_l_imagenet", "efficientnetv2_s_imagenet", "efficientnetv2_b0_imagenet", "efficientnetv2_b1_imagenet", "efficientnetv2_b2_imagenet", "densenet121_imagenet", "densenet169_imagenet", "densenet201_imagenet", "yolo_v8_xs_backbone_coco", "yolo_v8_s_backbone_coco", "yolo_v8_m_backbone_coco", "yolo_v8_l_backbone_coco", "yolo_v8_xl_backbone_coco", "vitdet_base_sa1b", "vitdet_large_sa1b", "vitdet_huge_sa1b", "videoswin_tiny_kinetics400", "videoswin_small_kinetics400", "videoswin_base_kinetics400", "videoswin_base_kinetics400_imagenet22k", "videoswin_base_kinetics600_imagenet22k", "videoswin_base_something_something_v2", "resnet50_v2_imagenet_classifier", "efficientnetv2_s_imagenet_classifier", "efficientnetv2_b0_imagenet_classifier", "efficientnetv2_b1_imagenet_classifier", "efficientnetv2_b2_imagenet_classifier", "mobilenet_v3_large_imagenet_classifier". If looking for a preset with pretrained weights, choose one of "resnet50_imagenet", "resnet50_v2_imagenet", "mobilenet_v3_large_imagenet", "mobilenet_v3_small_imagenet", "csp_darknet_tiny_imagenet", "csp_darknet_l_imagenet", "efficientnetv2_s_imagenet", "efficientnetv2_b0_imagenet", "efficientnetv2_b1_imagenet", "efficientnetv2_b2_imagenet", "densenet121_imagenet", "densenet169_imagenet", "densenet201_imagenet", "yolo_v8_xs_backbone_coco", "yolo_v8_s_backbone_coco", "yolo_v8_m_backbone_coco", "yolo_v8_l_backbone_coco", "yolo_v8_xl_backbone_coco", "vitdet_base_sa1b", "vitdet_large_sa1b", "vitdet_huge_sa1b", "videoswin_tiny_kinetics400", "videoswin_small_kinetics400", "videoswin_base_kinetics400", "videoswin_base_kinetics400_imagenet22k", "videoswin_base_kinetics600_imagenet22k", "videoswin_base_something_something_v2", "resnet50_v2_imagenet_classifier", "efficientnetv2_s_imagenet_classifier", "efficientnetv2_b0_imagenet_classifier", "efficientnetv2_b1_imagenet_classifier", "efficientnetv2_b2_imagenet_classifier", "mobilenet_v3_large_imagenet_classifier".
  • load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.
  • input_shape : input shape that will be passed to backbone initialization, Defaults to None.If None, the preset value will be used.

Example

# Load architecture and weights from preset
model = keras_cv.models.ImageClassifier.from_preset(
    "resnet50_imagenet",
)

# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.ImageClassifier.from_preset(
    "resnet50_imagenet",
    load_weights=False,
Preset name Parameters Description
resnet50_v2_imagenet_classifier 25.61M ResNet classifier with 50 layers where the batch normalization and ReLU activation precede the convolution layers (v2 style). Trained on Imagenet 2012 classification task.
efficientnetv2_s_imagenet_classifier 21.61M ImageClassifier using the EfficientNet smallarchitecture. In this variant of the EfficientNet architecture, there are 6 convolutional blocks. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 83.9% top 1 accuracy and 96.7% top 5 accuracy on imagenet.
efficientnetv2_b0_imagenet_classifier 7.20M ImageClassifier using the EfficientNet B0 architecture. In this variant of the EfficientNet architecture, there are 6 convolutional blocks. As with all of the B style EfficientNet variants, the number of filters in each convolutional block is scaled by width_coefficient=1.0 and depth_coefficient=1.0. Weights are initialized to pretrained imagenet classification weights. Published weights are capable of scoring 77.1% top 1 accuracy and 93.3% top 5 accuracy on imagenet.
efficientnetv2_b1_imagenet_classifier 8.21M ImageClassifier using the EfficientNet B1 architecture. In this variant of the EfficientNet architecture, there are 6 convolutional blocks. As with all of the B style EfficientNet variants, the number of filters in each convolutional block is scaled by width_coefficient=1.0 and depth_coefficient=1.1. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 79.1% top 1 accuracy and 94.4% top 5 accuracy on imagenet.
efficientnetv2_b2_imagenet_classifier 10.18M ImageClassifier using the EfficientNet B2 architecture. In this variant of the EfficientNet architecture, there are 6 convolutional blocks. As with all of the B style EfficientNet variants, the number of filters in each convolutional block is scaled by width_coefficient=1.1 and depth_coefficient1.2. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 80.1% top 1 accuracy and 94.9% top 5 accuracy on imagenet.
mobilenet_v3_large_imagenet_classifier 3.96M ImageClassifier using the MobileNetV3Large architecture. This preset uses a Dense layer as a classification head instead of the typical fully-convolutional MobileNet head. As a result, it has fewer parameters than the original MobileNetV3Large model, which has 5.4 million parameters.Published weights are capable of scoring 69.4% top-1 accuracy and 89.4% top 5 accuracy on imagenet.
图像分类器模型
ImageClassifier class
from_preset method