Initial model upload with Keras, SavedModel, H5, weights, and configs

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,65 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- tensorflow
+- keras
+- computer-vision
+- medical-imaging
+- brain-tumor
+- mobilevit
+- image-classification
+datasets:
+- brain-tumor-mri
+metrics:
+- accuracy
+model-index:
+- name: MobileViT Brain Tumor Classifier
+  results:
+  - task:
+      type: image-classification
+      name: Brain Tumor Classification
+    dataset:
+      type: brain-tumor-mri
+      name: Brain Tumor MRI Images
+    metrics:
+    - type: accuracy
+      value: 0.9850
+      name: Accuracy
+---
+
+# MobileViT Brain Tumor Classifier
+
+This MobileViT model classifies brain MRI scans into:
+
+- Healthy
+- Tumor
+
+Accuracy: **98.5%**
+
+⚠️ **Note**: For research/educational purposes only. Not for clinical use.
+
+## Model Files
+
+- `model.keras`: Native Keras format (recommended)
+- `model.h5`: Legacy H5 format
+- `saved_model/`: TensorFlow SavedModel format
+- `model.weights.h5`: Model weights only
+- `model_config.json`: Model architecture configuration
+- `class_names.json`: Class label mappings
+
+## Usage
+
+```python
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+
+# Download and load model
+model_path = hf_hub_download(repo_id="abdo1176/brain-model-test", filename="model.keras")
+model = tf.keras.models.load_model(model_path)
+
+# Or load weights only
+weights_path = hf_hub_download(repo_id="abdo1176/brain-model-test", filename="model.weights.h5")
+# model.load_weights(weights_path)
+```

class_names.json

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+[
+  "Healthy",
+  "Tumor"
+]

config.json

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+{
+  "model_type": "mobilevit",
+  "architecture": "MobileViT-XXS",
+  "num_classes": 2,
+  "input_shape": [
+    224,
+    224,
+    3
+  ],
+  "class_names": [
+    "Healthy",
+    "Tumor"
+  ],
+  "framework": "tensorflow",
+  "task": "image-classification",
+  "dataset": "brain-tumor-mri"
+}

model.h5

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+size 10353600

model.keras

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model.weights.h5

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preprocessing_config.json

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+{
+  "image_size": [
+    224,
+    224
+  ],
+  "normalization": "0-1",
+  "channels": 3,
+  "format": "RGB"
+}

saved_model/fingerprint.pb

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