added README.

README.md

@@ -1,3 +1,98 @@
 ---
 license: apache-2.0
+tags:
+  - brain-mri
+  - classification
+  - medical-imaging
+  - deep-learning
+  - cnn
+model-index:
+  - name: Brain MRI Classification - FLAIR Abnormality Classification
+    results:
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: LGG Segmentation Dataset
+          type: medical-imaging
+          link: https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation
+        metrics:
+          - type: accuracy
+            value: 0.958
+            name: Accuracy
 ---
+
+# Brain MRI Classification - FLAIR Abnormality Classification v1.0.0
+
+This repository hosts the trained model for **FLAIR Abnormality Classification** in Brain MRI scans. The model is a CNN-based architecture designed to classify Brain MRI images as normal or abnormal (FLAIR abnormality).
+
+## Model Details
+
+- **Architecture:** A CNN model that classifies whether a given Brain MRI image has abnormality.
+- **Dataset:** [LGG Segmentation Dataset](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation)
+- **Version:** v1.0.0
+- **Task:** Image Classification
+- **License:** Apache 2.0
+
+## Usage
+
+To use this model for inference, you can load it using the `tensorflow` library.
+
+```bash
+# Clones the repository and installs dependencies
+!git clone https://huggingface.co/preethamganesh/bms-flair-abnormality-classification-v1.2.0
+!pip install tensorflow
+
+# Imports TensorFlow
+import tensorflow as tf
+
+# Loads the pre-trained model from the cloned directory
+model_path = "bms-flair-abnormality-classification-v1.2.0"
+exported_model = tf.saved_model.load(model_path)
+
+# Retrieves the default serving function from the loaded model
+model = exported_model.signatures["serving_default"]
+
+# Prepares a dummy input tensor for inference (batch size: 1, height: 256, width: 256, channels: 3)
+input_data = tf.ones((1, 256, 256, 3), dtype=tf.float32)
+
+# Performs inference using the model. The output will be a dictionary, with the classification logits in the key 'output_0'
+output = model(input_data)["output_0"]
+
+# Prints the predicted class (e.g., 0 for normal, 1 for abnormal)
+predicted_class = tf.argmax(output, axis=-1).numpy()
+print("Predicted class: ", predicted_class)
+```
+
+## Training Details
+
+### Compute
+
+- The model was trained on a GeForce 4070Ti GPU with 16GB VRAM.
+- Training completed in approximately 1.8 minutes over 20 epochs.
+
+### Dataset
+
+- The model was trained on the [LGG Segmentation Dataset](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation), which includes Brain MRI images labeled for FLAIR abnormality segmentation.
+
+### Performance on test set
+
+- **Accuracy:** 0.958
+
+## Citation
+
+If you use this model in your research, please cite the repository:
+
+```bash
+@misc{preethamganesh2024brainmri,
+  title={Brain MRI Segmentation - FLAIR Abnormality Classification},
+  author={Preetham Ganesh},
+  year={2025},
+  url={https://huggingface.co/preethamganesh/brain-mri-flair-abnormality-classification-v1.2.0},
+  note={Apache-2.0 License}
+}
+```
+
+## Contact
+
+For any questions or support, please contact [email protected].