ImageNet pre-trained models with batch normalization for the Caffe framework
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Updated
Nov 26, 2017 - Python
ImageNet pre-trained models with batch normalization for the Caffe framework
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Fine-tuning an already learned model, adapts the architecture to other datasets
Pretrained VGG-16 network as feature extractor for Object Recognition (Python, Keras, Scikit-Learn)
QuickCNN is high-level library written in Python, and backed by the Keras, TensorFlow, and Scikit-learn libraries. It was developed to exercise faster experimentation with Convolutional Neural Networks(CNN). Majorly, it is intended to use the Google-Colaboratory to quickly play with the ConvNet architectures. It also allow to train on your local…
Implementation on tensorflow fine tuning of generic CNN based model
Ongoing minor project
Using Pytorch with Django To distinguish Cats from Dogs by Fine Tuning pretrained Model.
image processing task I did using fine-tuning, DCNNs and common data augmentation techniques. dataset consisted of 1600 x-ray images of human stomach which included 800 each of pylori(helicobacter) positive and negative. augmented the dataset using common data augmentation techniques.
DL pre-trained mode fine-tuning for cat-dog classification example
Fine-tuning Language Models with Conditioning on Two Human Preferences
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