MlLearnerAkash

AdPredictor

Training script for anomalydetection

age_gender_ethnicity

imgAugForLabelmeAnootation

Repo for augmenting labelme annotated data

Invisibility_Cloak

This is a fun application of image processing which enables you to experience the magic of an invisibility cloak. Let's make our childhood fantasy of using an invisibility cloak come true.

Logistic-_Regression_from_Scratch

I have implemented the logistic regression Model without using sklearn library and achieved 95% accuracy.

MlLearnerAkash

Config files for my GitHub profile.

object_detection-PLUS-distance_estimation-v1

This project implements object detection and range estimation. The object detection is done through YOLOV5 in this implementation. The project works in concatenation i.e object detection output is input to the range estimation model.

regression_mlops

mlops

Ridge_Regression

To show how to use Ridge Algorithm. And to show that Ridge Regression is superior to Vannila Linear Regression.

MlLearnerAkash/imgAugForLabelmeAnootation

Repo for augmenting labelme annotated data

MlLearnerAkash/regression_mlops

mlops

MlLearnerAkash/amazon-sagemaker-from-idea-to-production

MlLearnerAkash/auto-annotation

MlLearnerAkash/awesome-small-object-detection

🕶 A curated list of Tiny Object Detection papers and related resources.

MlLearnerAkash/cam2bev

TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.

MlLearnerAkash/CBIR

MlLearnerAkash/CentreFusion

Deep learning model for Camera and radar data fusion for 3d object detection and Birds eye view generation

MlLearnerAkash/detectron2-publaynet

Detectron2 for Document Layout Analysis for publaynet

MlLearnerAkash/ExplainableYOLOv7

Alpha release of explainable ai techniques for yoloV7, including eigenCAM, gradCam

MlLearnerAkash/floam

Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021

MlLearnerAkash/kalman-filter

MlLearnerAkash/layout-detection-fastapi

Object Detection Service Template. FastAPI for YOLOv8 in Docker.

MlLearnerAkash/layout-segmentation-yolo

YOLO models trained by DocLayNet - power your Document Intelligent by Layout Analysis

MlLearnerAkash/Live-Video-Streaming-with-Python-and-Flask

In this project Live Video Streaming is Done with Python Using Flask and Gunicorn as a production WSGI server with gevent for asynchronous calls and ngrok to tunnel the website on to public domain . It streams the video and also saves the stream.

MlLearnerAkash/med_equipement_track

Repo to detect and track different medical equipement

MlLearnerAkash/med_si_track

source code for surgical item tracking in OR

MlLearnerAkash/my_bot

Template for a ROS package

MlLearnerAkash/operVu_250323

MlLearnerAkash/pcSitPosture

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

MlLearnerAkash/perception_pipeline

ROS2 pipeline for cloud point manipulation

MlLearnerAkash/PyTorch-Simple-MaskRCNN

A PyTorch implementation of simple Mask R-CNN

MlLearnerAkash/segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

MlLearnerAkash/Spectral-Neural-Mapping

feature extractor using Frequency domain

MlLearnerAkash/SRFormer-Scene-Text-Det

[AAAI 2024] SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression

MlLearnerAkash/SwinDocSegmenter

[ICDAR 2023] (Oral) An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation

MlLearnerAkash/Textbooks-and-study-material

Some textbooks and reference books I used in my engineering course in Computer Science

MlLearnerAkash/Udacity-Sensor-fusion-python

This repo contains all the code presentd in classroom excercise in python

MlLearnerAkash/YOLO-text-detection

Proceed with text detection only in the selected area of ​​the image

MlLearnerAkash/yolov7_grad_cam