DM's repositories
ECCV-2020-Patch-based-CNN-for-bark-classification
Paper accepted at the ECCV 2020 Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020)
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Federated_object_detection
Federated Learning on object detection
few-shot-object-detection
Implementations of few-shot object detection benchmarks
Fewshot_Detection
Few-shot Object Detection via Feature Reweighting
Keras-FasterRCNN
keras implementation of Faster R-CNN
keras-frcnn
Keras Implementation of Faster R-CNN
MedicalZooPytorch
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
mlhub-tutorials
Tutorials to access Radiant MLHub Training Datasets
MultiTemplateMatching-Python
Object-recognition using multiple templates in python
nlp-recipes
Natural Language Processing Best Practices & Examples
object-detection-detectron2
A series of notebooks to dive deep into popular datasets for object detection and learn how to train Detectron2 on a custom dataset
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
retinanet-examples
Fast and accurate object detection with end-to-end GPU optimization
SOTA-MedSeg
SOTA medical image segmentation methods based on various challenges
split-folders
đź—‚ Split folders with files (i.e. images) into training, validation and test (dataset) folders
superpixel-benchmark
An extensive evaluation and comparison of 28 state-of-the-art superpixel algorithms on 5 datasets.
syndata-generation
Code used to generate synthetic scenes and bounding box annotations for object detection. This was used to generate data used in the Cut, Paste and Learn paper
transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.