Update of Model for Keras v3

.gitignore

@@ -1 +1,2 @@
-**/events.out*
+kaggle/
+wandb/

README.md

@@ -31,8 +31,11 @@ model-index:
           split: test # Optional. Example: test
         metrics:
           - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
-            value: 0.932 # Required. Example: 20.90
+            value: 0.927 # Required. Example: 20.90
             name: Test Accuracy # Optional. Example: Test WER
+          - type: hyperml/balanced_accuracy
+            value: 912
+            name: Test Balanced Accuracy
 ---
 
 # TODO: UPDATE WITH LATEST INFO
@@ -45,6 +48,8 @@ model-index:
 
 This is the model card for the SDH Model used by the [MyoQuant](https://github.com/lambda-science/MyoQuant) tool.
 
+**Note 10-06-2025: The model have been re-trained and made compatible with Keras v3**
+
 ## Intended uses & limitations
 
 It's intended to allow people to use, improve and verify the reproducibility of our MyoQuant tool. The SDH model is used to classify SDH stained muscle fiber with abnormal mitochondria profile.
@@ -56,34 +61,35 @@ It's trained on the [corentinm7/MyoQuant-SDH-Data](https://huggingface.co/datase
 ## Training procedure
 
 This model was trained using the ResNet50V2 model architecture in Keras.  
-All images have been resized to 256x256 using the `tf.image.resize()` function from Tensorflow.  
+All images have been resized to 256x256.  
 Data augmentation was included as layers before ResNet50V2.  
 Full model code:
 
 ```python
-data_augmentation = tf.keras.Sequential([
-  layers.RandomBrightness(factor=0.2, input_shape=(None, None, 3)), # Not avaliable in tensorflow 2.8
-  layers.RandomContrast(factor=0.2),
-  layers.RandomFlip("horizontal_and_vertical"),
-  layers.RandomRotation(0.3, fill_mode="constant"),
-  layers.RandomZoom(.2, .2, fill_mode="constant"),
-  layers.RandomTranslation(0.2, .2,fill_mode="constant"),
-  layers.Resizing(256, 256, interpolation="bilinear", crop_to_aspect_ratio=True),
-  layers.Rescaling(scale=1./127.5, offset=-1), # For [-1, 1] scaling
+data_augmentation = keras.Sequential([
+    keras.layers.RandomBrightness(factor=0.2, input_shape=(None, None, 3)),
+    keras.layers.RandomContrast(factor=0.2),
+    keras.layers.RandomFlip("horizontal_and_vertical"),
+    keras.layers.RandomRotation(0.3, fill_mode="constant"),
+    keras.layers.RandomZoom(.2, .2, fill_mode="constant"),
+    keras.layers.RandomTranslation(0.2, .2, fill_mode="constant"),
+    keras.layers.Resizing(256, 256, interpolation="bilinear", crop_to_aspect_ratio=True, input_shape=(None, None, 3)),
+    keras.layers.Rescaling(scale=1. / 127.5, offset=-1),  # For [-1, 1] scaling
 ])
 
 # My ResNet50V2
-model = models.Sequential()
+model = keras.models.Sequential()
 model.add(data_augmentation)
 model.add(
     ResNet50V2(
-    include_top=False,
-    input_shape=(256,256,3),
-    pooling="avg",
-  )
+        include_top=False,
+        input_shape=(256, 256, 3),
+        pooling="avg",
+    )
 )
-model.add(layers.Flatten())
-model.add(layers.Dense(len(config.SUB_FOLDERS), activation='softmax'))
+model.add(keras.layers.Dense(len(config.SUB_FOLDERS), activation='softmax'))
+
+model.summary()
 ```
 
 ```
@@ -99,9 +105,9 @@ _________________________________________________________________
  dense (Dense)               (None, 2)                 4098
 
 =================================================================
-Total params: 23,568,898
-Trainable params: 23,523,458
-Non-trainable params: 45,440
+Total params: 23,568,898 (89.91 MB)
+Trainable params: 23,523,458 (89.73 MB)
+Non-trainable params: 45,440 (177.50 KB)
 _________________________________________________________________
 ```
 
@@ -121,7 +127,9 @@ For more details please see the training notebook associated.
 
 ## Training Curve
 
-Full training results are avaliable on `Weights and Biases` here: [https://api.wandb.ai/links/lambda-science/ka0iw3b6](https://api.wandb.ai/links/lambda-science/ka0iw3b6)
+Full training results are avaliable on `Weights and Biases`
+here: [https://wandb.ai/lambda-science/myoquant-sdh](https://wandb.ai/lambda-science/myoquant-sdh)
+
 Plot of the accuracy vs epoch and loss vs epoch for training and validation set.
 ![Training Curve](./training_curve.png)
 
@@ -130,32 +138,24 @@ Plot of the accuracy vs epoch and loss vs epoch for training and validation set.
 Results for accuracy and balanced accuracy metrics on the test split of the [corentinm7/MyoQuant-SDH-Data](https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data) dataset.
 
 ```
-105/105 - 11s - loss: 0.1574 - accuracy: 0.9321 - 11s/epoch - 102ms/step
-Test data results:
-0.9321024417877197
-105/105 [==============================] - 6s 44ms/step
-Test data results:
-0.9166411912436779
+105/105 - 22s - 207ms/step - accuracy: 0.9276 - loss: 0.1678
+Test data results: 
+0.9276354908943176
+105/105 ━━━━━━━━━━━━━━━━━━━━ 20s 168ms/step
+Test data results: 
+0.9128566397981572
 ```
 
 # How to Import the Model
 
-With Tensorflow 2.10 and over:
+With Keras 3 / Tensorflow 2.19 and over:
 
 ```python
 model_sdh = keras.models.load_model("model.keras")
 ```
 
-With Tensorflow <2.10:
-To import this model RandomBrightness layer had to be added by hand (it was only introduced in Tensorflow 2.10.). So you will need to download the `random_brightness.py` fille in addition to the model.  
-Then the model can easily be imported in Tensorflow/Keras using:
-
-```python
-from .random_brightness import *
-model_sdh = keras.models.load_model(
-    "model.keras", custom_objects={"RandomBrightness": RandomBrightness}
-)
-```
+Resizing and rescaling layer are already implemented. You simply need to provide your images images numpy float32
+array (0-255).
 
 ## The Team Behind this Dataset
 

history.pickle

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pyproject.toml

@@ -6,15 +6,20 @@ readme = "README.md"
 requires-python = ">=3.12"
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     "matplotlib>=3.10.3",
     "notebook>=7.4.3",
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     "tensorflow>=2.19.0",
     "tensorflow-metal>=1.2.0",
+    "tf2onnx>=1.16.1",
+    "umap-learn>=0.5.7",
     "wandb>=0.19.11",
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