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Updated 3 years ago
!pip install jovian --upgrade --quietIdentifying the classes of different nature scenes around the world using Deep Learning and PyTorch.
Deep Learning is a branch of machine learning that uses a model architecture with deeply connected neurons to accurately identify features and relationships of classes, hence high prediction accuracy.
The following steps will be used to classify the images:
- Pick a dataset.
- Download the dataset.
- Import the dataset using PyTorch.
- Explore the dataset.
- Prepare the dataset for training.
- Move the dataset to the GPU.
- Define a deep learning model and its neural network.
- Train and validate the model using evaluation metrics.
- Test the model using new images.
- Conclude on the performance of the model.
!pip install opendatasets --upgradeCollecting opendatasets
Downloading https://files.pythonhosted.org/packages/18/99/aaa3ebec81dc347302e730e0daff61735ed2f3e736129553fb3f9bf67ed3/opendatasets-0.1.10-py3-none-any.whl
Requirement already satisfied, skipping upgrade: kaggle in /usr/local/lib/python3.6/dist-packages (from opendatasets) (1.5.10)
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Installing collected packages: opendatasets
Successfully installed opendatasets-0.1.10
I have selected the Intel Image Classification task that contains images of natural scenes around the world.
Download the Intel dataset using opendatasets from https://github.com/JovianML/opendatasets