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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: keras |
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pipeline_tag: image-classification |
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tags: |
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- astronomy |
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widget: |
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- src: https://huggingface.co/IT-Guy007/AstroVisionV1/blob/main/images/spiral-example.png |
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output: |
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text: one_one |
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--- |
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# Model Card for Model ID |
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This model classifies RGB images to the 2 classes, Spheroid or Spiral. |
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## Model Details |
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### Model Description |
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- **Developed by:** Jeroen den Otter, Michael Rutkowski |
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- **Funded by:** NASA |
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- **Model type:** Keras Sequential |
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- **License:** Apache2 |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/overview |
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## Uses |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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model = tf.keras.models.load_model('model.keras') |
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prediction = model.predict(image) |
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print(prediction) |
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``` |
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## Training Details |
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### Training Data |
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From the kaggle zoo challenge the classes one_one(Spheroid) 80%> and one_two(Spiral) 90%> are used. |
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Furthermore are the image segmented for noice removal |
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### Training Procedure |
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```python |
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train_ds, val_ds = image_dataset_from_directory( |
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dataset_directory, |
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validation_split=0.2, |
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subset="both", |
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seed=123, |
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image_size=(200, 200), |
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batch_size=32, |
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color_mode='grayscale' |
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) |
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data_augmentation = tf.keras.Sequential([ |
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tf.keras.layers.RandomFlip('horizontal'), |
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tf.keras.layers.RandomRotation(0.2), |
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tf.keras.layers.RandomZoom(0.2), |
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tf.keras.layers.RandomContrast(0.2), |
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tf.keras.layers.RandomBrightness(0.2), |
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tf.keras.layers.GaussianNoise(0.1), |
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]) |
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AUTOTUNE = tf.data.AUTOTUNE |
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train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) |
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val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE) |
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model = tf.keras.Sequential([ |
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data_augmentation, |
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tf.keras.layers.Rescaling(1./255, input_shape=(200, 200, 1)), |
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tf.keras.layers.Conv2D(32, (3, 3), activation='relu'), |
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tf.keras.layers.MaxPooling2D(2, 2), |
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tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), |
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tf.keras.layers.MaxPooling2D(2, 2), |
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tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), |
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tf.keras.layers.MaxPooling2D(2, 2), |
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tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), |
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tf.keras.layers.MaxPooling2D(2, 2), |
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tf.keras.layers.Flatten(), |
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tf.keras.layers.Dropout(0.5), |
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tf.keras.layers.Dense(512, activation='relu'), |
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tf.keras.layers.Dense(2, activation='softmax') |
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]) |
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), |
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loss='sparse_categorical_crossentropy', |
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metrics=['accuracy']) |
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``` |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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### Model summary |
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```text |
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ |
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┃ Layer (type) ┃ Output Shape ┃ Param # ┃ |
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┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ |
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│ sequential (Sequential) │ (None, 200, 200, 1) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ rescaling (Rescaling) │ (None, 200, 200, 1) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ conv2d (Conv2D) │ (None, 198, 198, 64) │ 640 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ max_pooling2d (MaxPooling2D) │ (None, 99, 99, 64) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ conv2d_1 (Conv2D) │ (None, 97, 97, 128) │ 73,856 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ max_pooling2d_1 (MaxPooling2D) │ (None, 48, 48, 128) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ conv2d_2 (Conv2D) │ (None, 46, 46, 256) │ 295,168 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ max_pooling2d_2 (MaxPooling2D) │ (None, 23, 23, 256) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ conv2d_3 (Conv2D) │ (None, 21, 21, 256) │ 590,080 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ max_pooling2d_3 (MaxPooling2D) │ (None, 10, 10, 256) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ flatten (Flatten) │ (None, 25600) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ dropout (Dropout) │ (None, 25600) │ 0 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ dense (Dense) │ (None, 1024) │ 26,215,424 │ |
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├─────────────────────────────────┼────────────────────────┼───────────────┤ |
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│ dense_1 (Dense) │ (None, 2) │ 2,050 │ |
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└─────────────────────────────────┴────────────────────────┴───────────────┘ |
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Total params: 81,531,656 (311.02 MB) |
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Trainable params: 27,177,218 (103.67 MB) |
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Non-trainable params: 0 (0 MB) |
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Optimizer params 54,354,438 (207.35 MB) |
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``` |
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### Loss and Accuracy |
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### Results |
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| | precision | recall | f1-score | support | |
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|-----------|-----------|--------|----------|---------| |
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| one_one | 0.98 | 0.98 | 0.96 | 1637 | |
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| one_two | 0.98 | 0.98 | 0.98 | 1740 | |
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| accuracy | | | 0.96 | 3377 | |
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| macro avg | 0.98 | 0.98 | 0.98 | 3377 | |
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| weighted avg| 0.98 | 0.98 | 0.98 | 3377 | |
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## Environmental Impact |
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- **Hardware Type:** M3 Pro |
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- **Hours used:** 72 |
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