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!pip install jovian --upgrade --quiet
pip install opendatasets --upgrade
Requirement already up-to-date: opendatasets in /usr/local/lib/python3.6/dist-packages (0.1.10) Requirement already satisfied, skipping upgrade: click in /usr/local/lib/python3.6/dist-packages (from opendatasets) (7.1.2) Requirement already satisfied, skipping upgrade: kaggle in /usr/local/lib/python3.6/dist-packages (from opendatasets) (1.5.10) Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.6/dist-packages (from opendatasets) (4.41.1) Requirement already satisfied, skipping upgrade: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (2.8.1) Requirement already satisfied, skipping upgrade: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (4.0.1) Requirement already satisfied, skipping upgrade: urllib3 in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (1.24.3) Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (2.23.0) Requirement already satisfied, skipping upgrade: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (2020.12.5) Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (1.15.0) Requirement already satisfied, skipping upgrade: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle->opendatasets) (1.3) Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle->opendatasets) (3.0.4) Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle->opendatasets) (2.10)

Identifying Flower Species using Deep Learning and Pytorch

we are going to do it in the following steps:
.Pick a dataset

  1. Pick a dataset
  2. Download the dataset
  3. Import the dataset using Pytorch
  4. Prepare the dataset for training
  5. Move the dataset to the GPU
  6. Define a neural networks
  7. Train the model
  8. Make predictions on sample images iterate on it with different networks & parameters
import opendatasets as od
dataset_url="https://www.kaggle.com/alxmamaev/flowers-recognition"