exploring image captioning
-
Updated
Aug 3, 2021 - Jupyter Notebook
8000
exploring image captioning
This repository hosts all the scripts used in the implementation of bird detection models. We are using Convolutional Neural Networks(CNN)'s Faster R-CNN, Single Shot Detector(SSD), and YOLOv3 meta-architectures while utilizing ResNet-101, MobileNet, Inception ResNet v2 and VGG-16 feature extraction Networks (backbone network).
This project focuses on detecting diseases in cotton plants using machine learning techniques. Early detection of diseases in cotton plants can help farmers take preventive measures and ensure better crop yields. The project uses a Convolutional Neural Network (CNN) based on ResNet152 architecture for image classification.
Dog breed classifier using Resnet150 and Pytorch
Simple object detection from camera using ResNet152
Udacity Deep Learning Nanodegree project
Advanced Topics in Machine Learning
ResNet Comparison for Garbage Image Classification with PyTorch - models trained on GPU, then pickled for analysis on CPU
Sketcha - Image Recognition web app made with Python and Flask
Fall 2021 Introduction to Deep Learning - Homework 2 Part 2 (face classification, face verification)
Cotton leaf disease prediction using ResNet-152v2 deep residual network architecture.
Local Intelligence Bootcamp organized by IIT Tirupati in collaboration with Stanford’s Computer Science & Social Good and Women in Computer Science.
Automatic Diagnosis of COVID-19 using CT Scan
Harness the power of U-Net and advanced interpolation techniques to extract LST, emissivity, and NDVI from satellite images. Transform. raw satellite data into meaningful insights for climate analysis, agriculture, and urban studies
A deep learning–based skin disease classification system using ResNet152V2 with a Streamlit web app and a Jupyter notebook for model training and testing.
This is an implementation of ResNet using keras.
Explainable AI (XAI) for image classifiers refers to a set of techniques used to make the decisions of "black box" deep learning models understandable to humans.
To develop a computer aided diagnosis tool to detect the presence of diabetic retinopathy and classify whether it is a normal diabetic retinopathy or an abnormal diabetic retinopathy.
Live Image Classification
Add a description, image, and links to the resnet-152 topic page so that developers can more easily learn about it.
To associate your repository with the resnet-152 topic, visit your repo's landing page and select "manage topics."