MIDAS_Internship

CNN Task Work : Please check "MIDAS-Internship.pdf" for further analysis

Added the Colab Jupyter Notebook for the work done.

Task 2 :

Use this dataset (https://www.dropbox.com/s/pan6mutc5xj5kj0/trainPart1.zip) to train a CNN. Use no other data source or pretrained networks, and explain your design choices during preprocessing, model building and training. Also, cite the sources you used to borrow techniques. A test set will be provided later to judge the performance of your classifier. Please save your model checkpoints.
Next, select only 0-9 training images from the above dataset, and use the pretrained network to train on MNIST dataset. Use the standard MNIST train and test splits (http://yann.lecun.com/exdb/mnist/). How does this pretrained network perform in comparison to a randomly initialized network in terms of convergence time, final accuracy and other possible training quality metrics? Do a thorough analysis. Please save your model checkpoints.
Finally, take the following dataset (https://www.dropbox.com/s/otc12z2w7f7xm8z/mnistTask3.zip), train on this dataset and provide test accuracy on the MNIST test set, using the same test split from part 2. Train using scratch random initialization and using the pretrained network part 1. Do the same analysis as 2 and report what happens this time. Try and do qualitative analysis of what's different in this dataset. Please save your model checkpoints.

Requiremets/Dependencies:

fastbook You can use fastai without any installation by using Google Colab. In fact, every page of this documentation is also available as an interactive notebook - click "Open in colab" at the top of any page to open it (be sure to change the Colab runtime to "GPU" to have it run fast!) See the fast.ai documentation on Using Colab for more information.

conda install -c fastai -c pytorch -c anaconda fastai gh anaconda miniconda : conda install -c fastai -c pytorch fastai pip install fastai

TRAINED MODELS:

TO LOAD THE MODELS FOR TESTING:

Since, its a fastai wrapper it's helpful to use their method.
Step1 : learn_inf = load_learner(path/'model_file')
Step2 : pred,pred_idx,probs = learn_inf.predict(img)
for multiple files : test = ImageList.from_folder(path/'test-jpg').add(ImageList.from_folder(path/'test-jpg-additional'))
learn = load_learner(path, test=test)
preds, _ = learn.get_preds(ds_type=DatasetType.Test)
This gives our predictions.

REFERENCES AND OTHER DETAILS CAN BE FOUND IN DOC:

https://arxiv.org/abs/1411.1792
https://www.jeremyjordan.me/nn-learning-rate/
https://arxiv.org/pdf/1812.01187.pdf
Hyperspherical Variational Auto-Encoders (Davidson, Falorsi, De Cao, Kipf, and Tomczak, 2018): https://www.researchgate.net/figure/Latent-space-visualization-of-the-10-MNIST-digits-in-2-dimensions-of-both-N-VAE-left_fig2_324182043
Analyzing and Improving the Image Quality of StyleGAN (Karras et al., 2020): https://arxiv.org/abs/1912.04958
Semantic Image Synthesis with Spatially-Adaptive Normalization (Park, Liu, Wang, and Zhu, 2019): https://arxiv.org/abs/1903.07291
Few-shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (Wu, Zhang, Xue, Freeman, and Tenenbaum, 2017): https://arxiv.org/abs/1610.07584
These Cats Do Not Exist (Glover and Mott, 2019): http://thesecatsdonotexist.com/
Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://arxiv.org/abs/1809.11096
PyTorch Documentation: https://pytorch.org/docs/stable/index.html#pytorch-documentation
MNIST Database: http://yann.lecun.com/exdb/mnist/

Thanks for the opportunity MIDAS LAB :)

charchit7/MIDAS_Internship