Added model card

README.md

@@ -1,3 +1,132 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+library_name: keras
+pipeline_tag: image-classification
+tags:
+- astronomy
+---
+# Model Card for Model ID
+
+This model classifies RGB images to the 2 classes, Spheroid or Spiral. 
+
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Jeroen den Otter
+- **Funded by:** NASA
+- **Shared by [optional]:** Michael Rutkowski
+- **Model type:** Keras Sequential
+- **Language(s) (NLP):** English
+- **License:** Apache2
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/overview
+- **Paper [optional]:** In progress
+
+## Uses
+
+The model can be used for identifying different galaxies from cutout images. It does not provide bounding boxes, so multiple galaxies in 1 image is not desired.
+
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+model = tf.keras.models.load_model('model.keras')
+prediction = model.predict(image)
+print(prediction)
+
+```
+
+## Training Details
+
+### Training Data
+
+From the kaggle zoo challenge the classes one_one(Spheroid) 80%> and one_two(Spiral) 90%> are used.
+
+Furthermore are the image segmented for noice removal
+
+### Training Procedure
+
+```python
+data_augmentation = tf.keras.Sequential([
+  tf.keras.layers.RandomFlip('horizontal'),
+  tf.keras.layers.RandomRotation(0.2),
+  tf.keras.layers.RandomZoom(0.2),
+  tf.keras.layers.RandomContrast(0.2),
+  tf.keras.layers.RandomBrightness(0.2),
+  tf.keras.layers.GaussianNoise(0.1),
+])
+
+AUTOTUNE = tf.data.AUTOTUNE
+train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
+val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
+
+model = tf.keras.Sequential([
+    data_augmentation,
+    tf.keras.layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
+    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Flatten(),
+    tf.keras.layers.Dropout(0.5),
+    tf.keras.layers.Dense(512, activation='relu'),
+    tf.keras.layers.Dense(num_classes, activation='softmax')
+])
+
+model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+```
+
+
+#### Training Hyperparameters
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+Test data is manual retrieved data from Hubble and James web, see manually manipulated data in the files and their accuracy. Of each a log and linear scaling is used.
+
+### Results
+              precision    recall  f1-score   support
+
+     one_one       0.96      0.98      0.96      1637
+     one_two       0.98      0.93      0.96      1740
+
+    accuracy                           0.96      3377
+   macro avg       0.96      0.96      0.96      3377
+weighted avg       0.96      0.96      0.96      3377
+
+
+## Environmental Impact
+
+
+- **Hardware Type:** M3 Pro
+- **Hours used:** 30min