license: mit
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.
Achieved a 93% validation accuracy
- Developed by: Aveek Goswami, Amos Koh
- Funded by [optional]: Nullspace Robotics Singapore
- Model type: Convolutional Neural Network
Model Sources
Uses
The tflite model (model.tflite) was loaded into a Raspberry Pi running a live object detection script.
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
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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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
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:
https://pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/
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