nuhhatipoglu Goto Github PK

active-learning-detect

Active learning + object detection

albrt

Cellular Composition Prediction in Routine Histology Images

alipy

ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.

cocoevalplus

A high-level encapsulation for evaluating detection on the Microsoft COCO dataset.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-performance-evaluation-tool

This project aims to provide the results in COCO dataset for different object detection models styles like Masked R-CNN, YOLO & SSD.

dmol-book

Deep learning for molecules and materials book

evaluate_google_openimages_using_coco_api

evaluation_coco

fastbook

The fastai book, published as Jupyter Notebooks

faster-rcnn-keras

这是一个faster-rcnn的keras实现的库，可以利用voc数据集格式的数据进行训练。

faster-rcnn-tf2

这是一个faster-rcnn的tensorflow2实现的库，可以利用voc数据集格式的数据进行训练。

fastercnn-pytorch-training-pipeline

A Faster RCNN Object Detection Pipeline for custom datasets using PyTorch.

latex-examples

Examples for the usage of LaTeX

mean_average_precision

Mean Average Precision for Object Detection

nuhhatipoglu

Config files for my GitHub profile.

python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

resunetplusplus-with-crf-and-tta

ResUNet++, CRF, and TTA for segmentation of medical images (IEEE JBIHI)

review_object_detection_metrics

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

tensorflow-2.x-yolov3

YOLOv3 implementation in TensorFlow 2.3.1

tf2-faster-rcnn

This is a fast and concise implementation of Faster R-CNN with TensorFlow2.

tf2-object-detection-api-tutorial-1

Tensorflow 2 Object Detection API Tutorial. This tutorial will take you from installation, to running pre-trained detection model, and training your model with a custom dataset, then exporting it for inference.

transformers-tutorials

This repository contains demos I made with the Transformers library by HuggingFace.

u-net-for-multi-class-semantic-segmentation

u-net-with-multiple-classification