virilo's Stars
Taeyoung96/Yolo-to-COCO-format-converter
Yolo to COCO annotation format converter
imankgoyal/NonDeepNetworks
Official Code for "Non-deep Networks"
abhishekkrthakur/colabcode
Run VSCode (codeserver) on Google Colab or Kaggle Notebooks
vk1996/colab_demos
waleedka/hiddenlayer
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.
mxbi/mlcrate
A python module of handy tools and functions, mainly for ML and Kaggle
bnu-wangxun/Deep_Metric
Deep Metric Learning
agermanidis/livepython
Visually trace Python code in real-time.
oleg-panichev/kinoa
lopuhin/kaggle-dsbowl-2018-dataset-fixes
Kaggle Data Science Bowl 2018 dataset fixes
pfnet-research/nips17-adversarial-attack
Submission to Kaggle NIPS'17 competition on adversarial examples (non-targeted adversarial attack track)
spanishkagglers/sk-bowl17
Spanish Kagglers Bowl 17
janishar/mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
shreyas0906/Object-detection-with-deep-learning-and-sliding-window
Introduces an approach for object detection in an image with sliding window. The repository contains three files, make_data.py reads the image in gray scale and converts the image into a numpy array. The labels are also appended based on the file name. In this case, if the file name starts with "trn", then 1 is appended else 0. Finally, all the images and labels are saved into .npy file. The test-model-1.py file loads the images and converts the labels into two categories as we are doing binary classification of images. The model is built using keras with theano as backend. In this case, the best training accuracy was 80% since the data was just 500 images and the testing accuracy was 67%