WebJun 10, 2024 · I am trying to build a food classification model with 101 classes. The dataset has 1000 image for each class. The accuracy of the model which I trained is coming less than 6%. I have tried implementing NASNet and VGG16 with imagenet weights but the accuracy did not increase. I have tried using Adam optimizer with or without amsgrad. WebApr 2, 2024 · Inception v3 is a deep convolutional neural network trained for single-label image classification on ImageNet data set. The TensorFlow team already prepared a …
Train your own image classifier with Inception in TensorFlow
WebApr 1, 2024 · This study makes use of Inception-v3, which is a well-known deep convolutional neural network, in addition to extra deep characteristics, to increase the … WebInception-v3 is a pre-trained convolutional neural network that is 48 layers deep, which is a version of the network already trained on more than a million images from the ImageNet database. This pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich … cell phone repair tuckernuck trail
Classify Large Scale Images using pre-tr…
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