Update README.md

README.md

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-# fruit-classification-pytorch
-Fruit classification using Kaggle Dataset [Fruit-360](https://www.kaggle.com/moltean/fruits) in pytorch
+# Fruit Classifier using Pytorch
+Fruit classification using Kaggle Dataset [Fruit-360](https://www.kaggle.com/moltean/fruits) in pytorch.
+This repository contains some code on :
+a) Creation of custom dataset using pytorch. Look at fruit.py to understand how the custom dataset can be prepared from a set of training and test images.
+b) Creation of a Network in pytorch which is simplier to create and try out any changes to it.
+c) Easy to train and test.
 
-## How to run the test
-Make sure to download the directory in a folder.
+# Training and Validation of Fruit-360 dataset.
 
-First load the datasets into npy files.
+## Step 1
+The same concept applies to all different kinds of datasets.
+Firstly, load all the images that are downloaded from the above link and convert them into npy files.
+Advantage of using npy files is to use only 4 files named train_data.npy, train_labels.npy and validation_data.npy , validation_labels.npy
+rather than using thousands of files for pre-processing.
+
+To convert your training and validation dataset into npy files use the below script.
 
 ```
 python load_dataset.py --dataset-dir <Dataset Path>
 ```
+
 This creates train_data.npy, train_labels.npy, validation_data.npy, validation_labels.npy
 
-Using this, train and test the fruit dataset using train.py
+## Step 2
+Use the dataset files that are created above, train the fruit classifier and evaluate the model.
 
 ```
-python train.py --data-dir <npy files folder>
+python train.py --data-dir <npy files folder> [--epochs <default:10>]
 ```
 
+This generates a log that trains the network for each epoch and finally do inference against the validation dataset spits out the validation accuracy. 
+
+