Image Classification
TF-Keras
English
resnet
medical
bio
biology
Pneumonia_detection
X-Ray_Classfication
Instructions to use ryefoxlime/PneumoniaDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use ryefoxlime/PneumoniaDetection with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("ryefoxlime/PneumoniaDetection") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Set `library_name` to `tf-keras`.
Verified signatureModel 'ryefoxlime/PneumoniaDetection' seems to be compatible only with "Keras 2" and not "Keras 3". To distinguish them, models compatible with legacy Keras 2.x should be tagged as `tf-keras` while models compatible with Keras 3.x are tagged as `keras`. This PR updates the model card to replace the explicit `library_name: keras` metadata which is now outdated by `library_name: tf-keras`. Updating this metadata will facilitate its discoverability and usage. For more information about `keras` and `tf-keras` library names, check out this pull request: https://github.com/huggingface/huggingface.js/pull/774.4915bf0
-
Update README.md
e8d3934 -
Update README.md
ba0d2be -
testing
1c740da -
testing
1b5ea11 -
Update preprocessor_config.json
8604ff2 -
Update config.json
57d35d7 -
Update config.json
5d6be06 -
Update README.md
58aa02e -
Update preprocessor_config.json
2449961 -
Create preprocessor_config.json
fd61d51 -
Update README.md
cf8e5ec -
Update config.json
a235ac8
-
Update README.md
bd7e1e7 -
Update README.md
7b2ba8f
-
Push Keras model using huggingface_hub.
3801f04 -
Create config.json
4827068 -
Update README.md
3849a74 -
Create README.md
2842a14 -
Pneumonia Detection with an accuracy of 91%
Model Architecture: - model = Sequential() - model.add(base_model) - model.add(AveragePooling2D(pool_size=(2, 2), strides=2)) - model.add(Flatten()) - model.add(Dense(256, activation="relu")) - model.add(Dense(128, activation="relu")) - model.add(Dense(2, activation="softmax")) - model.compile( loss="sparse_categorical_crossentropy", optimizer=Adam(learning_rate=0.000035), metrics=["sparse_categorical_accuracy"], ) Confusion Matrix for the model  model evaluation result: c7308c1 -
initial commit
3c6a538