[Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021

📰 Newspaper4k a fork of the beloved Newspaper3k. Extraction of articles, titles, and metadata from news websites.

Data Labeling, Tracking and Annotation with AI

Simplify Your Visual Data Ops. Find and visualize issues with your computer vision datasets such as duplicates, anomalies, data leakage, mislabels and others.

Anonymize sensitive data in your datasets.

Official Code for the dataset exploration of Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods

Datasets

kg-import automates the ingestion of heterogeneous datasets into a Knowledge Graph.

(Windows/Linux) Local WebUI for finetuning, evaluation and generation of neural network models (LLM and StableDiffusion) on python (In Gradio interface). Translated on 3 languages

Low Resource Context Relation Sampler for contexts with relations for fact-checking and fine-tuning your LLM models, powered by AREkit

Utility to making datasets of images and points coordinates that have been marked up on these images by user

CLI PHP for visualize Machine learning datasets in Graph bar format. Detect Outliers. See your data before Training

Make AVADataset custom dataset.

Automatic machine-learning dataset processing pipelines, using an LLM

While working on a Unet project, I created a program that can be used to add noise, a random grid (textbook) and a random shade of grey , this tool will output (depending on witch variation) combinations of two images the noisy image ut self and the clear one for the first variation (this one gave better results with Unet application) while the …

This repo can help people having trouble with extracting segmentation images and masks from replica and matterport3d-habitat

Check row data from csv to extract number & percentage of emtpy, null, na, nan values, extract the type of the value (string, numeric, date, ip, emtpy, null, na, nan). Count(empty cols), percentage(empty cols), zeros values, ....

The project for English texts complexity evaluation. Gives a descriptive statistics of a text complexity on different linguistic levels (phonological, morphological. syntactic, lexical, semantical ones). Made for teachers, students and linguists for the texts evaluation and mark-up

Conversations / Instructions Editor

ASNQ without trivial negative answers.