#Inception v3 pytorch finetune download#

ModelInfo(input_space='RGB', input_size=, input_range=, mean=, std=) Show preprocessing that was used to train the original model on ImageNet > model = make_model('resnext101_64x4d', num_classes=10, pretrained=True)

Model = make_model('vgg16', num_classes=10, pretrained=True, input_size=(256, 256), classifier_factory=make_classifier) Make a VGG16 model that takes images of size 256x256 pixels and uses a custom classifier import torch.nn as nnĭef make_classifier(in_features, num_classes): This information is needed to determine the input size of fully-connected layers. VGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images Make a VGG16 model that takes images of size 256x256 pixels Model = make_model('inceptionresnetv2', num_classes=10, pretrained=True, pool=nn.AdaptiveMaxPool2d(1)) Make a model with Global Max Pooling instead of Global Average Pooling import torch.nn as nn Make a model with Dropout model = make_model('nasnetalarge', num_classes=10, pretrained=True, dropout_p=0.5) Model = make_model('resnet18', num_classes=10, pretrained=True) Now call make_model('resnet18', num_classes=10) is equal to make_model('resnet18', num_classes=10, pretrained=True)Įxample usage: Make a model with ImageNet weights for 10 classes from cnn_finetune import make_model Default value for pretrained argument in make_model is changed from False to True.Squeeze-and-Excitation Networks ( senet154, se_resnet50, se_resnet101, se_resnet152, se_resnext50_32x4d, se_resnext101_32x4d).Inception-ResNet v2 ( inceptionresnetv2).ShuffleNet v2 ( shufflenet_v2_x0_5, shufflenet_v2_x1_0)įrom the Pretrained models for PyTorch package:.SqueezeNet ( squeezenet1_0, squeezenet1_1).DenseNet ( densenet121, densenet169, densenet201, densenet161).ResNet ( resnet18, resnet34, resnet50, resnet101, resnet152).Supported architectures and models From the torchvision package: Allows adding a Dropout layer or a custom pooling layer.Allows you to use images with any resolution (and not only the resolution that was used for training the original model on ImageNet).Automatically replaces classifier on top of the network, which allows you to train a network with a dataset that has a different number of classes.Gives access to the most popular CNN architectures pretrained on ImageNet.Finally the values are first rescaled to and then normalized using mean= and std=.Fine-tune pretrained Convolutional Neural Networks with PyTorch. The images are resized to resize_size= using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=. The inference transforms are available at Inception_V3_Weights.IMAGENET1K_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. These weights are ported from the original paper. weights='DEFAULT' or weights='IMAGENET1K_V1'. Inception_V3_Weights.DEFAULT is equivalent to Inception_V3_Weights.IMAGENET1K_V1. Finally the values are first rescaled to and then normalized using mean= and std=. The inference transforms are available at Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects.

Tench, goldfish, great white shark, … (997 omitted) These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1: weights='DEFAULT' or weights='IMAGENET1K_FBGEMM_V1'. Inception_V3_QuantizedWeights.DEFAULT is equivalent to Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1. The model builder above accepts the following values as the weights parameter. Inception_V3_QuantizedWeights ( value ) ¶ **kwargs – parameters passed to the 3īase class. Quantize ( bool, optional) – If True, return a quantized version of the model.

#Inception v3 pytorch finetune download#

Progress ( bool, optional) – If True, displays a progress bar of the download to stderr. Weights ( Inception_V3_QuantizedWeights or Inception_V3_Weights, optional) – The pretrained Quantized models only support inference and run on CPUs. Note that quantize = True returns a quantized model with 8 bit