Object Detection on the ExDark Dataset using image pre-processing and different object detection algorithms
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Updated
May 13, 2024 - Jupyter Notebook
Object Detection on the ExDark Dataset using image pre-processing and different object detection algorithms
This MATLAB project trains a deep learning model (RCNN) to detect stop signs in images. Train with your data & use the model to identify stop signs in new images with confidence scores. Great for exploring deep learning object detection!
中文长文本分类、短句子分类、多标签分类、两句子相似度(Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short),字词句向量嵌入层(embeddings)和网络层(graph)构建基类,FastText,TextCNN,CharCNN,TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN
A Benchmark of Text Classification in PyTorch
In this repository is my experimental thesis work on the recognition of museum works through object detection techniques.
Object Detection and Counting
An attempt at the speech emotion recognition (SER) task on the CREMA-D dataset using TensorFlow 1D & 2D RCNN models.
Trash Object Detection from Images or Videos with specific trash type labels using TACO dataset
A ML model for CONTRAN signal plates identification
Explore the cutting edge of computer vision with this comprehensive repository, showcasing a spectrum from classical machine learning to state-of-the-art transformer models.
Built a model to accurately segment the stomach and intestines in MRI scans, streamlining the cancer treatment process by automating the postioning of these organs, reducing the manual effort required.
ORCA: Oceanic Recognition & Classification Application for sea-life analysis systems.
Recursive Convolutional Neural Network (rCNN) is an innovative algorithm that I have personally developed. rCNN combines the power of convolutional neural networks (CNNs) with a recursive approach, introducing a novel architecture for feature extraction and representation learning.
Due to highly variying domain features of different underwater enviornment, the publically available datasets alone are not the best fit to train a deep learning algorithm to predict trash
This Project focuses on building an Object detection model that detects Litter from two geographical locations: Falkirk, Scotland and Punalur, India
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