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.

Upload an image to find your Bollywood celebrity look-alike

Image Processing

Machine learning-VGG16-Numtadb_Dataset

Using the Convolutional Neural Network algorithm to label satellite image chips with different atmospheric conditions

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.

Tensorflow implementation of Algorithmic Style Transfer Paper 2015

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

This Repository contains TensorFlow implementation of different Image Segmentation Architecture on different types of datasets.

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

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%

Deep Learning Techniques