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
Code examples for training AlexNet using Keras and Theano
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)
An all-in-one Deep Learning toolkit for image classification to fine-tuning pretrained models using MXNet.
This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.
Code for our paper "Generalized Orderless Pooling Performs Implicit Salient Matching" published at ICCV 2017.
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…
Keras implementation of multi-label classification of movie genres from IMDB posters
Mask RCNN model for instance segmentation of power cables for infrastructure inspection purposes.
Switching from GPU to the future of Machine learning the TPU. Over 1 million images trained Resnet50 in under 20 mins compared to days or weeks on GPU and all for 0$ free on Google Colab Notebooks in Google Drive, clone repo and jump right in!!
animal is good
Image classification using both non-DL and DL approaches. Some interesting techniques are included like SIFT-feature extraction and multiple kernel learning (MLK).
DL pre-trained mode fine-tuning for cat-dog classification example
Ongoing minor project
Online machine learning competition for high school students.
A Convolution Neural Network Model for predicting various types of food.
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