Srihari Humbarwadi's repositories
DeepLabV3_Plus-Tensorflow2.0
DeepLabV3+ implemented in TensorFlow2.0
YOLOv1-TensorFlow2.0
A tensorflow2.0 implementation of the YOLOv1 paper https://arxiv.org/pdf/1506.02640
retinanet-tensorflow2.x
TensorFlow2.x implementation of RetinaNet
image_colorization_gan_tf2.0
A TensorFlow2.0 implementation of https://arxiv.org/abs/1803.05400
adain-tensorflow2.x
TensorFlow2.x implementation of Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
cartoon_gan_tf2.0
A DCGAN implementation in tensorflow 2.0, trained on cartoonset100k dataset
tensorflow_fcos
FCOS: Fully Convolutional One-Stage Object Detection
tensorflow2.0_notebooks
exploring new tensorflow 2.0 API
srgan_tensorflow
TensorFlow2.x implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
tf_cv_tutorials
Building computer vision models with TensorFlow2.x
tf-gsoc-2021
Final Submission for GSoC 20201
maskrcnn_demo
calculating pixel-wise accuracy for maskrcnn outputs
rl_algorithms
Implementations of algorithms discussed in the book Reinforcement Learning: An Introduction
LinkNet-Exploiting-Encoder-Representations-for-Efficient-Semantic-Segmentation
Keras implementation of https://arxiv.org/abs/1707.03718
NaturalLanguageRecommendations
Our submission to #TFWorld TF 2.0 Challenge!
qat_tensorflow2.x
Quantization Aware Training for Tensorflow2.x
vgg_normalized
VGG with normalized weights
ARS
An implementation of the Augmented Random Search algorithm
bootFromExternalStorage
Shell scripts to setup a NVIDIA Jetson AGX Xavier or Jetson Xavier NX Developer Kit to boot from external storage.
GATE
Welcome to GATE - Generalization After Transfer Evaluation - A framework built to evaluate a learning process on its ability to learn and generalize on previously unseen Tasks, Data domains and Modalities.
iaml-labs
IAML Labs Repository
inf-thesis-latex-cls
A LaTeX Class for Informatics theses at The University of Edinburgh
minimal-ml-template
A very minimal ml project template that uses HF transformers and wandb to train a simple NN and evaluate it, in a stateless manner compatible with Spot instances kubernetes workflows
models
Models and examples built with TensorFlow
neural-structured-learning
Training neural models with structured signals.