|
--- |
|
license: mit |
|
--- |
|
|
|
# Model Card for lego-technic-sorting-model |
|
Classification of lego technic pieces under basic room lighting conditions |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
CNN designed from the ground up, without using a pre-trained model to classify images of lego pieces into 7 categories. <br> |
|
Achieved a 93% validation accuracy |
|
|
|
- **Developed by:** Aveek Goswami, Amos Koh |
|
- **Funded by:** Nullspace Robotics Singapore |
|
- **Model type:** Convolutional Neural Network (CNN) |
|
|
|
### Model Sources |
|
|
|
- **Repository:** https://github.com/magichampz/lego-sorting-machine-ag-ak |
|
|
|
|
|
## Uses |
|
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. |
|
|
|
Example of real time object detection and classification: |
|
 |
|
|
|
|
|
## Bias, Limitations and Recommendations |
|
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. --> |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
- **Data:** https://huggingface.co/datasets/magichampz/lego-technic-pieces <br> |
|
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. --> |
|
|
|
### Training Procedure |
|
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 |
|
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. |
|
|
|
|
|
## Evaluation |
|
|
|
### 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. |
|
|
|
 |
|
|
|
|
|
|
