Update README.md

README.md

@@ -21,145 +21,40 @@ Achieved a 93% validation accuracy
 ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/652dc3dab86e108d0fea458c/E7UZXLWPvU_39cxrF49jD.gif)
 
 ## Uses
-The tflite model (model.tflite) was loaded into a Raspberry Pi running a live object detection script. <br>
-The Pi could then detect lego technic pieces in real time as the pieces rolled on a conveyor belt towards the Pi Camera
+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. 
+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
-The images of the lego pieces used to train the model were taken in 
+The images of the lego pieces used to train the model were taken in room lighting conditions, illuminated with a torchlight. <br>
+To use the model, would recommend  trying to recreate the conditions and achieve photographs with a similar lighting. <br>
+Otherwise, it might be better to retrain the model with a new dataset of images corresponding to your lighting conditions
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
-### Recommendations
-
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-
 ## Training Details
 
 ### Training Data
 - **Data:** https://huggingface.co/datasets/magichampz/lego-technic-pieces
+More images can be taken by editing the motion_detection_and_image_classification.py script.
 
 <!-- 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. -->
 
-[More Information Needed]
-
 ### 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.
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+#### Preprocessing
+Images were normalized before being fed into the model. Their contrast was also increased using the increase_contrast_more function defined in the notebook attached.
 
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
 ### Results
+Our model was trained with 6000 images across 7 different categories of lego technic pieces, split into a 80/20 train/test split. <br>
+It achieved 93% testing accuracy and graphs of the accuracy and loss are shown below. <br>
+A confusion matrix was also plotted to visualize the performance of the classification algorithm. It depicts the count value of true versus false predictions across each category.
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-Trained on Google Collabs using the GPU available
-
-#### Hardware
-
-Model loaded into a raspberry pi 3 connected to a PiCamera v2 <br>
-RPi mounted on a holder and conveyor belt set-up built with lego
-
-
-## Citation
-Model implemented on the raspberry pi using the ideas from PyImageSearch's blog: <br>
-https://pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+![Unknown-5](https://user-images.githubusercontent.com/91732309/190358182-58fa5671-263d-490b-8f54-616cb2daf764.png)