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license: mit
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# Model Card for Model ID
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 [optional]:** Nullspace Robotics Singapore
- **Model type:** Convolutional Neural Network

### Model Sources

- **Repository:** https://github.com/magichampz/lego-sorting-machine-ag-ak
![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

## Bias, Limitations and Recommendations
The images of the lego pieces used to train the model were taken in 
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### Recommendations


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## Training Details

### Training Data
- **Data:** https://huggingface.co/datasets/magichampz/lego-technic-pieces

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### Training Procedure 

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#### Preprocessing [optional]

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#### 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 -->

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## Evaluation

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### Results

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#### Summary



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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### 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/

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