Update README.md

README.md

@@ -13,7 +13,7 @@ Achieved a 93% validation accuracy
 
 - **Developed by:** Aveek Goswami, Amos Koh
 - **Funded by [optional]:** Nullspace Robotics Singapore
-- **Model type:** Convolutional Neural Network
+- **Model type:** Convolutional Neural Network (CNN)
 
 ### Model Sources
 
@@ -21,9 +21,9 @@ Achieved a 93% validation accuracy
 ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/652dc3dab86e108d0fea458c/E7UZXLWPvU_39cxrF49jD.gif)
 
 ## Uses
-The files in the computer folder are meant for use on your own computer. 
-You can create and train your own deep learning model using your own data and also test this model on your computer. 
-The model was trained on Google colab, so create_training_data_array.py was used to upload data in the form of a numpy array to Google colab. 
+The files in the create-model folder are meant to be used on your own computer.
+You can train your own deep learning model using your own data and  test this model on your computer using testing-tflite-model.py on a single image.
+The model was trained on Google Colab, so create_training_data_array.py was used to creata a numpy array file to upload data in the form of a numpy array to Google Colab. 
 After transfering the tflite model to your Pi, you can then run the image classification file in the raspberry-pi folder to detect and classify lego pieces in real time.
 
 ## Bias, Limitations and Recommendations
@@ -41,7 +41,7 @@ More images can be taken by editing the motion_detection_and_image_classificatio
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
 ### Training Procedure 
-The model was trained using the GPU's available on Google Collab. The jupyter notebook loaded the data from a npy file (in the dataset card), which contained all the images as well as their category labels.
+The model was trained using the GPU's available on Google Colab. The jupyter notebook loaded the data from a npy file (in the dataset card), which contained all the images as well as their category labels.
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
 #### Preprocessing