49.5 mAP50 Detector enet4y2-coco.cfg = EfficientnetB0 + 4YOLO Layers + BiDirectionalFeatureMap with COCO Dataset and 81.0 mAP50 with VOC2007 test Dataset.
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Sep 1, 2020 - C
49.5 mAP50 Detector enet4y2-coco.cfg = EfficientnetB0 + 4YOLO Layers + BiDirectionalFeatureMap with COCO Dataset and 81.0 mAP50 with VOC2007 test Dataset.
This is my capstone project that was completed as part of the General Assembly Data Science Immersive curriculum. I conceptualized the idea, executed the project and developed a prototype for predicting success in the South Korean drama industry based on face image. This serves as a proof-of-concept which can definitely be developed further.
INR Denomination Recognition is an image classification project
I'm developing an app named BarkRescue, which includes project code, app functionalities, and system architecture. Additionally, I've written three detailed blogs on EfficientNet, YOLOv5, and MobileNet-v2, focusing on their architecture and workings before integrating these models into my project.
The purpose of Food Vision project is to classify 101 variety of food items using Machine Learning.
A multi classification using scikit-learn and TensorFlow models on MRI scans of patient's brains.
Image Captioning using EfficientNet and GRU
A project on building deep learning classifier to classify playing cards
An implementation of the Arabic sign language classification using Keras on the zArASL_Database_54K dataset
HAM10000 Skin Lesion Classification
Development of a depth estimation model based on a UNET architecture - connection of Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet.
Dust detection on solar photovoltaics panel using pre-trained CNN models
Clasificación de imágenes y reconocimiento de objetos mediante la red neuronal convolucional CNN DenseNet y EfficientNet con el modelo frozen model y el framework Coffe. Posteriomente, mediante la red neuronal convolucional CNN MobileNet-SSD y YOLO con el framework TensorFlow
The model employs mixed-precision training within the TensorFlow framework, utilizing transfer learning techniques that encompass both feature extraction and fine-tuning stages. This approach is executed on the EfficientNetB0 architecture
Efficient way to detect if a face has a mask on. Used EfficientNet-B0 model architecture.
American Sign Language Alphabet Detection in Real Time with EfficientNetB0 in PyTorch
Development and analysis of various deep NN models to detect glaucoma cases from fundus images. The performance of the best model was evaluated with cross-validation. Mean F1-score: 0.95975, with a standard deviation of 0.02274.
Implementation of a neural network for solving the problem of mushroom classification based on the TensorFlow library and the pre-trained efficientnet/b0 model.
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