ml4sci/deeplense

We are pleased to announce the Machine Learning for Science (ML4SCI) 2021 Hackathon Competition that will take place from November 8-22, 2021. The ML4SCI competition is hosted by several campuses in the US including Puerto Rico and across the world, and is open to everyone. Participants will have the option of competing online or in-person. The hackathon will focus on applying machine learning techniques to scientific challenges, including those from the fields of physics, astronomy and planetary science.

Please find the competition poster at this link: https://bit.ly/3vOEtyN

The agenda for the hackathon can be found here: https://indico.cern.ch/event/1085878/

Anyone interested in learning more about machine learning techniques and trying their hand at the competition is welcome. Participants are encouraged to self-organize into small teams or work on their own to devise unique solutions to the challenge(s). The participants can work on the challenges on their own schedule. The competition will run for two weeks, however only a small fraction of that time is needed to obtain competitive results. Participants will have opportunities to interact with the organizers and with each other in person, via Zoom and on Slack. The kickoff meeting will be on Monday, November 8 (5 PM EDT) in person and on Zoom https://bit.ly/2YzCty3. There will also be an introductory machine learning lecture by Prof. Harrison Prosper on Tuesday, November 9 (2:30 PM EDT).Please see the Hackathon Slack page for in-person meeting details. Winners will receive certificates and prizes.

Interested participants can register via Slack at https://bit.ly/3arK2t2

There are six main challenges:

• Higgs Boson Challenge (Classification, General)

• Particle Images Challenge (Classification, Computer Vision)

• Strong Lensing Challenge (Multi-class Classification, Regression, Computer Vision)

• NMR Spin Challenge (Multi-Target Regression)

• Planetary Albedo (Regression, Image Analysis)

• Circumgalactic Medium (Dimensionality Reduction, Spectra)

Please find more details and example Jupyter notebooks inside each challenge folder.

Solutions are due on Monday, November 22 at 7pm EDT/6pm Central time. Winners will receive certificates and prizes. For any questions about the ML4SCI Hackathon please contact Prof. Sergei Gleyzer [email protected]

deeplense's Issues

Implementing Plant Disease Prediction using ResNet Model

Description:
I am working on implementing a plant disease prediction system using the ResNet model. I plan to use the HAGGLE dataset for training the model. The goal is to train the model on a cloud platform to leverage its computing power and scalability.

Tasks:

• Download the KAGGLE dataset.
• Preprocess the dataset for training.
• Implement the ResNet model architecture.
• Train the model on a cloud platform.
• Evaluate the model's performance.
• Deploy the trained model for inference.

Features:

• Importing necessary libraries:
  • import os for working with files
  • import numpy as np for numerical computations
  • import pandas as pd for working with dataframes
  • import torch for PyTorch module
  • import matplotlib.pyplot as plt for plotting information on graphs and images using tensors
  • import torch.nn as nn for creating neural networks
  • from torch.utils.data import DataLoader for dataloaders
  • from PIL import Image for checking images
  • import torch.nn.functional as F for functions for calculating loss
  • import torchvision.transforms as transforms for transforming images into tensors
  • from torchvision.utils import make_grid for data checking
  • from torchvision.datasets import ImageFolder for working with classes and images
  • from torchsummary import summary for getting the summary of our model

Labels: machine learning, cloud computing, enhancement

Push DeepLeense Regression Code

I would like to push the code for DeepLense Regression to DeepLense_Regression_Yurii_Halychanskyi/, but I need the "push access" for it. Could you give it to me?

Thanks!