MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights='imagenet') base_model.summary() # 冻结基础模型的所有层,使其在训练过程中不更新权重 base_model.trainable = False # 定义分类标签的数量 num_label = 5 # 假设有5个类别 # 使用 Functional API 构建模型 inputs = keras.Input(...
2)MobileNetV2可以从头训练或者利用预训练模型进行训练: w=1ifw:base_model=MobileNetV2(weights='imagenet',include_top=False,input_shape=(width,height,3))else:base_model=MobileNetV2(weights=None,include_top=False,input_shape=(width,height,3)) 第四步:统计正确率 正确率高达93.2% 第五步:搭建GUI界面...
net = mobilenetv2('Weights','imagenet') lgraph = mobilenetv2('Weights','none') Description MobileNet-v2 is a convolutional neural network that is 53 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database[1]. The pr...
# 加载 mobilenetv2模型 (model1)fromkeras.preprocessingimportimagefromkeras.applications.mobilenetv2importMobileNetV2fromkeras.applications.mobilenetv2importdecode_predictions,preprocess_inputimportnumpyasnp model1=MobileNetV2(weights='imagenet')size=224# 加载我最喜欢的resnet50模型 (model2)fromkeras.applications...
[224,224,3], alpha=1.0, include_top=True, weights='imagenet', classes=1000): rows = input_shape[0] img_input = Input(shape=input_shape) # stem部分 # 224,224,3 -> 112,112,32 first_block_filters = _make_divisible(32 * alpha, 8) x = ZeroPadding2D(padding=correct_pad(img_...
model.load_weights('mobilenet_v2_weights.h5') 1. 步骤5:进行图像分类或目标检测 现在,我们已经准备好使用MobileNetV2进行图像分类或目标检测了。下面的代码展示了如何使用模型对一张图像进行分类,并显示结果。 importcv2importnumpyasnp# 加载图像image=cv2.imread('image.jpg')# 对图像进行预处理processed_image=...
weights='imagenet') mobileNet.summary() Model:"mobilenetv2_1.00_224"___ Layer (type) Output Shape Param# Connected to=== input_1 (InputLayer) [(None,224,224,
load(model_weight_path, map_location=device) # 删除参数 (如果修改了模型结构,可以直接剔除预训练权重中不属于state_dict的参数,然后更新state_dict) pre_dict = {k: v for k, v in pre_weights.items() if net.state_dict()[k].numel() == v.numel()} net.load_state_dict(pre_dict, strict=...
# Create the base model from the pre-trained model MobileNet V2 IMG_SHAPE = IMG_SIZE + (3,) base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet') 1. 2. 3. 4. 5. #This feature extractor converts each 160x160x3 image into a ...
base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) # 冻结基础模型的层 base_model.trainable = False # 添加自定义层 x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) # 假设有1024个输出类别 prediction...