Distinguish bees from wasps
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Updated
May 18, 2021 - Jupyter Notebook
Distinguish bees from wasps
In this project, we propose a cervical cancer detection and classification system using CNNs . We employ transfer learning and fine-tuning for enhanced performance. Classifiers like ELM and AE are added to increase the efficiency.
The way people speak tells a lot about how they feel; we can discern if someone is pleased or sad as humans, but computers face a struggle. Deep-learning algorithms will be useful in order to translate this critical component of the communication. The main goal of this project is to use deep-learning algorithms to recognize the speaker's gender …
Trap Camera with positioning system and classification of images by animal species
This project focuses on the task of image classification using datasets sourced from Kaggle. The primary goal of this repository is to evaluate the performance of two neural network architectures, AlexNet and VGG16, and to draw comparisons between these methods.
Codes depicting Usage of Keras Library to Create CNN Models, Perform Fine-Tuning and Data-Augmentation on Image Datasets.
VGG16 is a Convolution Neural Net (CNN ) Architecture which was used to win ILSVR(ImageNet) Competition in 2014. It is considered to be one of the excellent vision model architecture till date.
Using VGG16 feature extractor with Scikit-learn Support Vector Machine to train the model to classify dogs and cats. Model accuracy: 94.71%
Image Classification model for detecting and classifying " DIABETIC RETINOPATHY" .
Creating a Sequential CNN model to classify images of various datasets and comparing the results to pretrained models (VGG16 and Inception V3). A dashboard design for the CNN model for the prediction
Image caption generator project is automatically describes images with coherent and relevant textual captions.
This is my graduate thesis, a mobile applicaiton with computer vision
Brain tumor detection and prediction using keras vgg-16
This project focuses on leveraging deep learning techniques to predict lung disease, specifically pneumonia, from chest X-ray images.
This project focuses on classifying the gender of individuals from facial images. It employs a combination of techniques including transfer learning, fine-tuning, and custom CNN models.
Assignment from "Convolutional Neural Networks", course #4 of Deep Learning Specialization (Coursera).
MNIST is the de facto “hello world” dataset of computer vision. In this competition, our goal is to correctly identify digits from a dataset of handwritten images.
A web application using Flask and the VGG16 deep learning model. It takes a video (max: 10Mb) and the user supplies the name of any object and the application using the VGG16 model and OpenCV to split the video into frames, analyses each frame and then returns all the frames that contain the object.
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