Instructions to use Auguzcht/malaria-detection-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Auguzcht/malaria-detection-cnn with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Auguzcht/malaria-detection-cnn") - Notebooks
- Google Colab
- Kaggle
Malaria Detection - Custom CNN Model
Model Description
This is a custom Convolutional Neural Network (CNN) trained to detect malaria parasites in cell images. The model classifies blood cell images as either Parasitized or Uninfected.
Model Architecture
- 3 Convolutional layers with ReLU activation
- MaxPooling layers for downsampling
- Dropout layer (0.5) for regularization
- Dense layers for classification
- Binary output with sigmoid activation
Training Details
- Dataset: Cell Images for Detecting Malaria
- Input Size: 150x150x3
- Optimizer: Adam
- Loss Function: Binary Crossentropy
- Epochs: 10
- Validation Split: 20%
Performance
- Validation Accuracy: {history.history['val_accuracy'][-1]:.4f}
- Validation Loss: {history.history['val_loss'][-1]:.4f}
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support