Create README.md

Author: Null-byte-00

README.md

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+# Image Recognition
+![alt text](https://github.com/Null-byte-00/Imagerecognition/blob/main/imagerec.gif)
+A simple Image recognition program using pytorch
+This is a simple convolutional neural network capable of recognizing 3 different shapes (house, smiling face and fish)<br>
+[you can download full version from here](https://drive.google.com/file/d/1CN_dbHrfiZctm-eJKfEIBof6HHYs1SjA/view?usp=drive_link)
+## The model 
+the model is a a small convolutional neural network with two convolution/maxpool and 2 linear layers. the input is a square 500x500 2d array (because all pixels in my datase images are whether (0,0,0) or (255,255,255) it shoul be 
+a 2d array of 0s and 1s). It gives 3 different numbers as output that are then passed through a softmax function to get the probabilities for 3 different shapes
+## The dataset
+the dataset consists of 243 pictures I drew (81 samples for each shape)<br>
+create_dataset.py turns all thesepictures intoa hdf file<br>
+[here you can download raw images](https://drive.google.com/file/d/1k1mj3IhPsY-ZmQHdGsiiAwm3iMY7Zt4R/view?usp=drive_link)
+## Training 
+the model is trained by stochastic gradient descent and mean square error loss function.<br>
+[here you can download the trained model pth file]()
+## how to use
+first import the conda enviroment
+```
+conda env create -f environment.yml
+```
+activate the enviroment
+```
+conda activate imagerecognition
+```
+create a directory named models then download the trained model from [here](https://drive.google.com/file/d/15cgj57P1yLuC7fwWc9u_2omlSK4DFuKl/view?usp=drive_link) and copy it in models directory
+now run:
+```
+python main.py
+```
+the window will open and you can draw. press 'c' on the keyboard to get the results like this:
+```
+tensor([-160.3708,  134.9981,   40.6356])
+house: tensor(0., grad_fn=<MulBackward0>)
+smilingface: tensor(100., grad_fn=<MulBackward0>)
+fish: tensor(1.0445e-39, grad_fn=<MulBackward0>)
+```
+these are probability percentages for each shape