detectron2 backbone: resnet18, efficientnet, hrnet, mobilenet v2, resnest, bifpn
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Updated
Apr 20, 2023 - Python
detectron2 backbone: resnet18, efficientnet, hrnet, mobilenet v2, resnest, bifpn
Image classification done with Mindspore technology
This is a clean, opinionated and minimalist implementation of the training and evaluation protocols for a fancy new self supervised learning (SSL) method called maximum manifold capacity representations.
It is the image classification task to classify Diabetic-Retinopathy category using ResNet18, ResNet50 pretrained model. which is related to kaggle competition. The kaggle competition link can found below. https://www.kaggle.com/c/diabetic-retinopathy-detection#description
First capstone project I worked on as part of the 'Programming in Python for AI' nanodegree at Udacity.
🔪 Elimination based Lightweight Neural Net with Pretrained Weights
ResNet18 model for image classification using Jupyter notebooks
Experimental framework for approximate computing on CNNs: pruning, quantization, sparsity analysis on ResNet-18 and MobileNetV2.
Training neural nets with quantized weights on arbitrarily specified bit-depth
Reconstructs the original sequence of frames from a jumbled video using deep learning (ResNet-18) and optimization (TSP) for high-accuracy video restoration.
A Python command line application that trains an image classifier on a given dataset and then using the trained model to predict new images.
Convert Pytorch model to Tensorflow lite model
🩺 This repository is made to contain a deep learning-based pneumonia detection system from chest X-ray images
Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning
Gender detection using gender classification model
AI-based land use / land cover classification over the Delhi Airshed using Sentinel-2 imagery, ESA WorldCover labels and a ResNet18 deep learning model.
Dogs Image Classifier classifies pet images to dogs and not dogs and identify the breeds of the classified dogs using transfer learning via PyTorch pretrained models.
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