Instructions to use FatimaDataScientist72/aquaculture-fish-disease-tinyml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use FatimaDataScientist72/aquaculture-fish-disease-tinyml with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://FatimaDataScientist72/aquaculture-fish-disease-tinyml") - Notebooks
- Google Colab
- Kaggle
Aquaculture Fish Disease TinyML Benchmark
Baseline (EfficientNetB0) and lightweight (MobileNetV3Small) models for multi-class freshwater fish disease classification, in float32 and INT8 (TFLite) formats. Companion artifact to a benchmarking paper on energy-aware model compression for edge-deployed aquaculture monitoring.
Trained on the "Freshwater Fish Disease Aquaculture in South Asia" dataset (Kaggle).
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