Waste_classification_Machine_Learning

Explored transfer learning with various pretrained models on waste classification dataset Evaluated the usage semi‐supervised learning using pseudo labelling. With the best model based on accuracy and precision, built a Fast API web server. Created a website in Svelte, upon capturing an image, made calls to the FastAPI to make predictions in the frontend. Deployed the trained model to the google cloud platform and used it instead for the backend API calls.

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
__pycache__		__pycache__
gcp		gcp
models		models
waste_class_frontend_svelte		waste_class_frontend_svelte
.DS_Store		.DS_Store
README.md		README.md
Untitled.ipynb		Untitled.ipynb
augmented-model-building-transfer-learning-vgg19.ipynb		augmented-model-building-transfer-learning-vgg19.ipynb
grid-search.ipynb		grid-search.ipynb
main.py		main.py
model-building-transfer-learning-resnet50.ipynb		model-building-transfer-learning-resnet50.ipynb
model-building-transfer-learning-vgg19.ipynb		model-building-transfer-learning-vgg19.ipynb
pb_to_keras_model_conv.ipynb		pb_to_keras_model_conv.ipynb
react-cors.json		react-cors.json
semisupervised.ipynb		semisupervised.ipynb
transfer-learning-vgg19.ipynb		transfer-learning-vgg19.ipynb
waste_class.code-workspace		waste_class.code-workspace

VasigaranS/Waste_classinfication_Machine_Learning

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Waste_classification_Machine_Learning

If you want to check out my work: https://waste-class-frontend-svelte.vercel.app/

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