Instructions to use Srivardhan369/Tumor_detection_ML_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Srivardhan369/Tumor_detection_ML_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://Srivardhan369/Tumor_detection_ML_Model") - Notebooks
- Google Colab
- Kaggle
Tumor Detection ML Model
Model Description
This model is designed to classify brain tumor images using a Convolutional Neural Network (CNN). It has been trained and fine-tuned on a labeled dataset of brain tumor MRI images.
Training Details
- Framework: TensorFlow/Keras
- Optimizer: Adam with a learning rate scheduler
- Loss Function: Categorical Crossentropy
- Data Augmentation: Includes rotation, width/height shift, zoom, and horizontal flipping.
- Hyperparameter Tuning: Performed using Keras Tuner.
Metrics
The following metrics were used to evaluate the model's performance:
- Accuracy: Measures the overall correctness of predictions.
- F1 Score: Balances precision and recall.
- Precision: Indicates the proportion of true positives among positive predictions.
- Recall: Indicates the proportion of true positives among all actual positives.
Usage
You can load the model using the Hugging Face Transformers library:
from transformers import AutoModel
model = AutoModel.from_pretrained("YourUsername/Tumor_detection_ML_Model")
- Downloads last month
- 4