Instructions to use feldaega17/xray-classifier-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use feldaega17/xray-classifier-model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://feldaega17/xray-classifier-model") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| library_name: keras | |
| language: | |
| - en | |
| pipeline_tag: image-classification | |
| # Chest X-Ray Classifier (Keras) | |
| This repository hosts a Keras model file (`best_mod.keras`) used by the corresponding Hugging Face Space demo. | |
| - Predictions: `Atelectasis`, `Effusion`, `Infiltration` | |
| - Input: RGB image resized to 224x224 and normalized | |
| - Preprocessing: CLAHE enhancement applied to the L channel in LAB color space | |
| ## Usage (Python) | |
| ```python | |
| import tensorflow as tf | |
| from huggingface_hub import hf_hub_download | |
| model_path = hf_hub_download(repo_id="<your-username>/<your-model-repo>", filename="best_mod.keras") | |
| model = tf.keras.models.load_model(model_path) | |
| ``` | |