The training data set contains 87,000 images which are 200x200 pixels. There are 29 classes, of which 26 are for the letters A-Z and 3 classes for SPACE, DELETE and NOTHING. These 3 classes are very helpful in real time applications, and classification. The test data set contains a mere 29 images, to encourage the use of real world test images.

The dataset is trained on resent34 and resnet50

Managed to get and accuracy of 99.9%

The 10 classes to predict are safe driving,texting - right,talking on the phone - right, texting - left, talking on the phone - left,operating the radio,drinking,reaching behind,hair and makeup,talking to passenger.

The model is trained on resnet34 architecture

After so much experimentation, I managed to get an accuracy of 99.5%

Total number of images are 82213, Training set size is 61488 images (one fruit or vegetable per image), Test set size is 20622 images (one fruit or vegetable per image), Multi-fruits set size is 103 images (more than one fruit (or fruit class) per image),Number of classes totally are 120 (fruits and vegetables).

The model is trained on resnet34 architecture

After so much experimentation, I managed to get an accuracy of 99.9%

The dataset for training the model is taken from following paper Oxford-IIIT Pet Dataset by O. M. Parkhi et al., 2012 which features 12 cat breeds and 25 dogs breeds. My model now need to learn to differentiate between these 37 distinct categories. According to their paper, the best accuracy they could get in 2012 was 59.21%, using a complex model that was specific to pet detection, with separate "Image", "Head", and "Body" models for the pet photos.

The model is trained on resnet34 architecture

After so much experimentation, I managed to get an accuracy of 96%

I used a pretarined on a bigger dataset(a cleaned subset of wikipedia called wikitext-103).

That model has been trained to guess what the next word,its input being all the previous words.It has a recurrent structure and a hidden state that is updated each time it sees a new word.This is hidden state thus contains information about the sentence up to that point. This is where unlablled data is going to useful to us, as we use it to fine tune our model.

Responsive Layout

User Friendly

Voice Enable task setting

Notes Writing

Inbuilt calendar availble

Many todolist applications available on playstore, Only some apps are user friendly and easily accessible. This app is designed in such a way that every user should experience a feeling of comfort while using this app.