wastewater-sludge-image-classification-cloud-gpus
IrfanKarim352
Reproducible deep-learning image-classification pipeline for industrial soft sensing. Originally built for sludge-cake quality monitoring at DC Water Blue Plains AWWTP. Six architectures (FastViT, EfficientNet, MobileNet, EfficientFormerV2, DeepTEN-ResNet, sparse-AE CNN) compared with multi-seed statistics on Modal cloud GPUs.
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3 data points · 2026-07-22 → 2026-08-28How do you feel about this project?
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Question copied to clipboardWhat is the IrfanKarim352/wastewater-sludge-image-classification-cloud-gpus GitHub project? Description: "Reproducible deep-learning image-classification pipeline for industrial soft sensing. Originally built for sludge-cake quality monitoring at DC Water Blue Plains AWWTP. Six architectures (FastViT, EfficientNet, MobileNet, EfficientFormerV2, DeepTEN-ResNet, sparse-AE CNN) compared with multi-seed statistics on Modal cloud GPUs.". Written in Python. Explain what it does, its main use cases, key features, and who would benefit from using it.
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