11 skills found
deadskull7 / Pneumonia Diagnosis Using XRays 96 Percent RecallBEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.
deepak2233 / Waste Or Garbage Classification Using Deep LearningThis model is created using pre-trained CNN architecture (VGG16 and RESNET50) via Transfer Learning that classifies the Waste or Garbage material (class labels =7) for recycling.
aqibsaeed / On Device Activity RecognitionPersonalized machine learning on the smartphone
CommissarMa / Pytorch CRAFT本项目旨在以CRAFT提供的预训练模型为基础，进行迁移学习以用于检测自己数据集中的文本。
AnMol12499 / CropcareAIAn AI-driven platform offering crop recommendations, fertilizer suggestions, and disease detection for optimal farming
UWNETLAB / Dcss SupplementarySupplementary materials for McLevey 2021 Doing Computational Social Science (Sage, UK).
ThanhHung2112 / LMS NextGenAn AI assistant for a Learning Management System (LMS)
ZJUFanLab / ScSpacean integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo space with spatial transcriptome references
llyx97 / TAMT[NAACL 2022] "Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training", Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weipinng Wang, Jie Zhou
kssr1345 / Qunatum Tranfer Learning For Diabetic RethinpathyIndia is on track to become the world’s diabetes capital thus demanding accurate diagnosis of Diabetic retinopathy from optical coherence tomography (OCT) retinal images. Accurate and faster diagnosis is difficult as it depends on quality of image, operator handling and also the growing number of patients. In this paper we propose the use of quantum transfer learning model to accomplish diagnosis of Diabetic Retinopathy. Quantum Transfer Learning (QTL), is a hybrid combination of classical transfer learning and quantum computing. Unlike classical computers, quantum computers provide faster computation and better accuracy. The concept of QTL is mainly used where the dataset size is limited. The QTL model, diagnostically significant image features are extracted with Resnet18 Convolutional Neural NEtwork (CNN) model, which is reduced to 4-bit feature vector to be encoded as qubit and is finally classified by utilizing Variational Quantum Circuit (VQC). The proposed model gave a better accuracy than existing state of the art methods in terms of high accuracy despite with a smaller set of images in the training phase.
diegoperea20 / Knee Xray Keypoints Detection YOLOV8Training and validation with yolov8 with the Knee Xray roboflow dataset (keypoint labeling) with tranfer learning with pose estimation.
