Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning)
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Updated
Oct 18, 2017 - Python
Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning)
Android app containing an Image classifier based on transfer learning CNN using Tensorflow 1.4.1 on Stanford's Imagenet cars dataset
A Distributed ResNet on multi-machines each with one GPU card.
image label predictor using keras and flask, for training used imagenet dataset with resnet 50.
We use pretrained networks VGGnet, AlexNet, GoogLeNet, ResNet which trained on the ImageNet dataset as a feature extractor to classify images.
make custom-defined skin dataset with ISIC 2017 medical images and ImageNet
scripts for downloading images form imagenet open images with labels
A demo for mapping class labels from ImageNet to COCO.
Convolution Neural Network Profiler
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
Tensorflow Faster R-CNN for Windows and Python 3.5
Based on the Google Deep Dream Inception V3 model
VGG16 Net implementation from PyTorch Examples scripts for ImageNet dataset
Deep Learning model which uses Computer Vision and NLP to generate captions for images
Class Activation Map (CAM)
This Tensorflow model classifies 8 categories of images. InceptionResNetV2 is the source network chosen to build the model and the ImageNet dataset is the source domain it has been pre-trained. The model performs classification with an accuracy of 99%.
Supporting scripts to evaluate different TensorFlow/Keras models on ImageNet (ILSCRV2012) classification dataset.
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