Instructions to use starpreeda/BrainTumorTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use starpreeda/BrainTumorTest with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://starpreeda/BrainTumorTest") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -114,6 +114,7 @@ Input (224, 224, 3)
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↳ Dropout(0.4)
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↳ Dense(4, activation='softmax')
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---
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## 🚀 How to Load and Use
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import os
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from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization
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from tensorflow.keras.models import Model
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model_url = "https://huggingface.co/starpreeda/BrainTumorTest/resolve/main/efficientnetb0_finetuned_brain_mri.keras"
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weights_path = "model_weights.keras"
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if not os.path.exists(weights_path):
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print("Downloading model weights...")
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urllib.request.urlretrieve(model_url, weights_path)
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print("Download completed!")
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base_model = EfficientNetB0(weights=None, include_top=False, input_shape=(224, 224, 3))
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x = base_model.output
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x = GlobalAveragePooling2D()(x)
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x = Dense(256, activation='relu')(x)
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x = Dropout(0.4)(x)
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outputs = Dense(4, activation='softmax')(x)
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model = Model(inputs=base_model.input, outputs=outputs)
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model.load_weights(weights_path)
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print("Model is ready for use!")
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↳ Dropout(0.4)
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↳ Dense(4, activation='softmax')
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---
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```
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## 🚀 How to Load and Use
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import os
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from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization
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from tensorflow.keras.models import Model
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model_url = "[https://huggingface.co/starpreeda/BrainTumorTest/resolve/main/efficientnetb0_finetuned_brain_mri.keras](https://huggingface.co/starpreeda/BrainTumorTest/resolve/main/efficientnetb0_finetuned_brain_mri.keras)"
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weights_path = "model_weights.keras"
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if not os.path.exists(weights_path):
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print("Downloading model weights...")
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urllib.request.urlretrieve(model_url, weights_path)
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print("Download completed!")
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base_model = EfficientNetB0(weights=None, include_top=False, input_shape=(224, 224, 3))
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x = base_model.output
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x = GlobalAveragePooling2D()(x)
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x = Dense(256, activation='relu')(x)
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x = Dropout(0.4)(x)
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outputs = Dense(4, activation='softmax')(x)
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model = Model(inputs=base_model.input, outputs=outputs)
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model.load_weights(weights_path)
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print("Model is ready for use!")
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