In this project i used Densenet for image classification in PyTorch with custom sampling function for pytorch imbalanced-dataset-sampler
Final Project Notebook
Download Anaconda
| Linux | Mac | Windows | |
|---|---|---|---|
| 64-bit | 64-bit (bash installer) | 64-bit (bash installer) | 64-bit (exe installer) |
| 32-bit | 32-bit (bash installer) | 32-bit (exe installer) |
Install Anaconda on your machine. Detailed instructions:
Please go though this doc before you creating an environment. After that create a environment using following command
conda create --name deep-learning
Then activate the environment using following command
activate deep-learning
These instructions also assume you have git installed for working with Github from a terminal window, but if you do not, you can download that first with the command:
conda install git
Now, you can create a local version of the project
- Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.
git clone https://github.com/koushik-elite/Using-Densenet-and-PyTorch.git
cd Using-Densenet-and-PyTorch
-
Install PyTorch and torchvision; this should install the latest version of PyTorch.
- Linux or Mac:
conda install pytorch torchvision -c pytorch- Windows:
conda install pytorch -c pytorch pip install torchvision -
Install a few required pip packages, which are specified in the requirements text file (including OpenCV).
pip install -r requirements.txt
- That's it!, Now run the project using following command, check you default browser and open "Using Densenet and PyTorch.ipynb" file
jupyter notebook
Approach for Image Classification
Curently iam working on t-Distributed Stochastic Neighbor Embedding
kaggle-1-winning-approach-for-image-classification-challenge from Kumar Shridhar
t-Distributed Stochastic Neighbor Embedding from Laurens van der Maaten
transfer-learning-the-art-of-fine-tuning-a-pre-trained-model from DISHASHREE GUPTA