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license: mit |
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# Model Card for Model ID |
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Classification of lego technic pieces under basic room lighting conditions |
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## Model Details |
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### Model Description |
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CNN designed from the ground up, without using a pre-trained model to classify images of lego pieces into 7 categories. <br> |
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Achieved a 93% validation accuracy |
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- **Developed by:** Aveek Goswami, Amos Koh |
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- **Funded by [optional]:** Nullspace Robotics Singapore |
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- **Model type:** Convolutional Neural Network |
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### Model Sources |
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- **Repository:** https://github.com/magichampz/lego-sorting-machine-ag-ak |
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## Uses |
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The tflite model (model.tflite) was loaded into a Raspberry Pi running a live object detection script. <br> |
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The Pi could then detect lego technic pieces in real time as the pieces rolled on a conveyor belt towards the Pi Camera |
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## Bias, Limitations and Recommendations |
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The images of the lego pieces used to train the model were taken in |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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## Training Details |
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### Training Data |
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- **Data:** https://huggingface.co/datasets/magichampz/lego-technic-pieces |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **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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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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Trained on Google Collabs using the GPU available |
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#### Hardware |
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Model loaded into a raspberry pi 3 connected to a PiCamera v2 <br> |
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RPi mounted on a holder and conveyor belt set-up built with lego |
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## Citation |
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Model implemented on the raspberry pi using the ideas from PyImageSearch's blog: <br> |
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https://pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/ |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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