model = torch.hub.load('pytorch/vision:v0.13.0', 'mobilenet_v2', weights='MobileNet_V2_Weights.DEFAULT') model.eval() print(model) Using cache found in /home/vmuser/.cache/torch/hub/pytorch_vision_v0.6.0 MobileNetV2( (features): Sequential( (0): ConvBNReLU( (0): Conv2d(3...
to(device) # 使用pytorch提供的预训练权重 https://download.pytorch.org/models/mobilenet_v2-b0353104.pth model_weight_path = "./mobilenet_v2-b0353104.pth" assert os.path.exists(model_weight_path), "file {} dose not exist.".format(model_weight_path) pre_weights = torch.load(model_weight...
High level network definitions with pre-trained weights in TensorFlow deep-learning tensorflow model vgg yolo faster-rcnn densenet resnet object-detection zoo squeezenet inception mobilenet yolov2 nasnet mobilenetv2 yolov3 pnasnet mobilenetv3 efficientnet Updated Jan 2, 2021 Python kuan...
本例提取了植物幼苗数据集中的部分数据做数据集,数据集共有12种类别,今天我和大家一起实现tensorflow2.X版本图像分类任务,分类的模型使用MobileNetV2,MobileNetV2在MobileNetV1的基础上增加了线性瓶颈(Linear Bottleneck)和倒残差(Inverted Residual)是一种轻量级的网络,适合应用在真实的移动端应用场景。 关于MobileNetV2的...
StereoVision-SLAM is a real-time visual stereo SLAM (Simultaneous Localization and Mapping) - Mobilenetv2 and resnet18 ONNX weights for creating mobilenet and resn… · rerun-io/StereoVision-SLAM@e0fe6af
- If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier) include_top: whether to include the fully-connected layer at the top of the network. weights: one of...
[1, 1, 1, 1], explicit_paddings=[], padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true](FeatureExtractor/MobilenetV2/MobilenetV2/input, FeatureExtractor/MobilenetV2/Conv/weights). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating ...
Hi, I am trying to use DL Workbench to do the INT8 optimization for my model, MobileNetV2 (trained from Tensorflow). I wish to optimize it using
"Weights for input shape (224, 224) will be " "loaded as the default." ) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = ...
parser.add_argument('--output', type=str, default='result.png', help='output image') parser.add_argument('--model', type=str, default='model/keras_Realtime_Multi_Person_Pose_Estimation_model.h5', help='path to the weights file') ...