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automatic-counting-of-epidermal-cells-with-deep-learning

Con este proyecto, se busca avanzar en el uso de modelos de Deep learning para la investigación en el campo de la botánica, ofreciendo una solución más eficaz y precisa para la medición de células epidérmicas.

bitnet

Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch

breast-cancer-toolkit

The repository provides code for running inference with different breast cancer models, links for downloading the trained model checkpoints, and example notebooks on how work with a DICOM pipeline.

breast-segmentation-yolov8-

Este repositorio presenta una implementación de segmentación de mamas utilizando YOLOv8. La segmentación de mamas es crucial en el diagnóstico de cáncer de mama. El proyecto incluye un notebook detallado y una demostración desplegada para probar el modelo en tiempo real.

customer-queues-in-c-plus-plus

empower-your-website-with-a-custom-llama3-chatbot

Create your own personalized chatbot experience directly within your web page using Llama3. This Streamlit-based application allows you to interact with a chatbot trained on your specified web content, providing tailored responses to your queries.

fine-tuning-sam-segmentation-on-custom-dataset-for-medical-images-

This repository is inspired by Mazurowski Lab's finetune-SAM. In this repository, we provide a simple conversion of COCO datasets to the SAM format necessary for fine-tuning. Additionally, we include Weights & Biases (wandb) visualization for tracking experiments and have fixed various code errors.

semantic-segmentation-for-breast-cancer-self-labeled-unannotated-data-with-sam

Explore our solution for breast cancer imaging segmentation using SAM. Autonomous labeling of unannotated data improves accuracy in mammary region segmentation, advancing cancer diagnostics

sentiment-analysis-of-tourist-site-reviews-transformers

MeIA Challenge 2023: Sentiment Analysis of Tourist Site Reviews - A project focused on sentiment analysis of tourist reviews in Mexican, Cuban, and Colombian destinations. The challenge addresses sentiment classification in imbalanced datasets and aims to enhance understanding of tourist experiences. Dataset, code, and evaluation tools included.