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README.md
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Upon exploring potential datasets, we found that there was no existing data which included various ingredients within the same scene in addition to quantitative labeling data for each ingredient. We decided to work with a small subset of potential ingredients for our project, to minimize the volatility of our demo-model and prove the efficacy of this feature. During the Hackathon, we acquired 3 produce items and a bag of chips from a local grocery store, which we then used to capture image data and label the quantities of said items.
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We utilized the Jupyter server provided by Intel to fine-tune, and deployed on a IDC compute instance with a Small VM - Intel® Xeon 4th Gen ® Scalable processor to connect with our frontend.
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## Model Details
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- **Model Name**: Moondream Fine-tuned Variant
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Upon exploring potential datasets, we found that there was no existing data which included various ingredients within the same scene in addition to quantitative labeling data for each ingredient. We decided to work with a small subset of potential ingredients for our project, to minimize the volatility of our demo-model and prove the efficacy of this feature. During the Hackathon, we acquired 3 produce items and a bag of chips from a local grocery store, which we then used to capture image data and label the quantities of said items.
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## Intel Tools
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We utilized the Jupyter server provided by Intel to fine-tune, and deployed on a IDC compute instance with a Small VM - Intel® Xeon 4th Gen ® Scalable processor to connect with our frontend.
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We combined both the gemma_xpu_finetuning.ipynb script and a script for fine-tuning Moondream in order to fine-tune Moondream with IPEX configuration on Intel servers.
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## Model Details
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- **Model Name**: Moondream Fine-tuned Variant
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