Pinned Repositories
100-days-of-code
Fork this template for the 100 days journal - to keep yourself accountable (multiple languages available)
Ableton-On-Linux
A bash script to install Ableton Live 11 Suite on Linux through Wine
analise-de-serie-temporal
Awesome-Meta-Learning
A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
bayesian_changepoint_detection
Methods to get the probability of a changepoint in a time series.
Building-Computer-Vision-Projects-with-OpenCV4-and-CPlusPlus
Implement complex computer vision algorithms and explore deep learning and face detection
draw-YOLO-box
Draw bounding boxes on raw images based on YOLO format annotation. Help to check the correctness of annotation and extract the images with wrong boxes.
GFPGAN
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
kcc
KCC (a.k.a. Kindle Comic Converter) is a comic and manga converter for ebook readers.
Rice-crop-Insects-and-Weed-Detection-using-faster-R-CNN
As the increase in the world population the demand of the rice is also increases. In order to increase the growth of rice in the rice crop it is necessary to detect the weed and insects in the rice crop to minimize the growth of weed and insects so that the growth of the rice can be increased.Insect and Weed detection is the important factor to be analyzed. Unmanned Air Vehicle (UAV) is used for data acquisition of rice crop in different phases and states so that high quality of RGB images can be captured. In which we have taken 15 different types of rice crop insects species images and different phases of weed images to train the model. The proposed method facilitates the extraction of weed and insects into the rice crop field using deep learning concept faster region-based convolutional neural networks(Faster R-CNNs) it is implemented using Python3 with the help of Tensorflow API. The result shows that Faster R-CNN method is the state of arts method for detection and classification of weed and insects with good accuracy rate.
jessflopes's Repositories
jessflopes/kcc
KCC (a.k.a. Kindle Comic Converter) is a comic and manga converter for ebook readers.
jessflopes/Ableton-On-Linux
A bash script to install Ableton Live 11 Suite on Linux through Wine
jessflopes/analise-de-serie-temporal
jessflopes/Awesome-Meta-Learning
A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
jessflopes/bayesian_changepoint_detection
Methods to get the probability of a changepoint in a time series.
jessflopes/Building-Computer-Vision-Projects-with-OpenCV4-and-CPlusPlus
Implement complex computer vision algorithms and explore deep learning and face detection
jessflopes/draw-YOLO-box
Draw bounding boxes on raw images based on YOLO format annotation. Help to check the correctness of annotation and extract the images with wrong boxes.
jessflopes/GFPGAN
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
jessflopes/Rice-crop-Insects-and-Weed-Detection-using-faster-R-CNN
As the increase in the world population the demand of the rice is also increases. In order to increase the growth of rice in the rice crop it is necessary to detect the weed and insects in the rice crop to minimize the growth of weed and insects so that the growth of the rice can be increased.Insect and Weed detection is the important factor to be analyzed. Unmanned Air Vehicle (UAV) is used for data acquisition of rice crop in different phases and states so that high quality of RGB images can be captured. In which we have taken 15 different types of rice crop insects species images and different phases of weed images to train the model. The proposed method facilitates the extraction of weed and insects into the rice crop field using deep learning concept faster region-based convolutional neural networks(Faster R-CNNs) it is implemented using Python3 with the help of Tensorflow API. The result shows that Faster R-CNN method is the state of arts method for detection and classification of weed and insects with good accuracy rate.
jessflopes/AutoEncoder-1
jessflopes/chatbot
criando um mini chatbot com javascript e php
jessflopes/CreditCardFraud
jessflopes/Crop_and_weed_detection
we made the crop and weed detection model using YOLOV3 on agricultural image data.
jessflopes/DeepLearning
jessflopes/desenvolvimento_continuo_ML
jessflopes/DL_gan_aula
jessflopes/DPG
Decision Predicate Graph (DPG) is a model-agnostic tool to provide a global interpretation of tree-based ensemble models.
jessflopes/IA-HPC
jessflopes/IBM-WATSON-CHATBOT-EMBED-SCRIPT
I've done IBM Watson assistant for the website! I've attached the embed code here!
jessflopes/intro_NLP
Aulas de introducão à NLP
jessflopes/introducao-CNN
jessflopes/Introducao-DeepLearning
jessflopes/JARVIS-Python
Assistente virtual J.A.R.V.I.S com reconhecimento de voz offline
jessflopes/pca_svc
jessflopes/proxy-list
proxy list that updates every 30 minutes
jessflopes/PULSE-Self-Supervised-Photo-Upsampling-via-Latent-Space-Exploration-of-Generative-Models
jessflopes/river
🌊 Online machine learning in Python
jessflopes/TCPDBench
The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data
jessflopes/VirtualAssistant_Helena
Projeto de PI da Fatec - Assistente virtual Helena
jessflopes/zap-gpt-free
Este projeto explora a integração do ChatGPT com o WhatsApp, transformando o chatbot em um assistente virtual capaz de realizar tarefas como falar com amigos, responder a perguntas de clientes, e muito mais, com um toque de humanização nas conversas.