| | --- |
| | license: mit |
| | library_name: keras |
| | tags: |
| | - dragon-detection |
| | - Keras |
| | - dragon |
| | - image-classification |
| | --- |
| | |
| | ## Dragon detector with Tensor Flow |
| | This is a simple `tensorflow` model to detect dragon in images. |
| | If you just want to test the trained model, make sure you have the following packages: |
| |
|
| | ``` |
| | tensorflow keras sklearn-deap datasets transformers[torch] sentencepiece |
| | ``` |
| |
|
| | ## Predict |
| |
|
| | To run prediction you need to run below code: |
| |
|
| | ```python |
| | from huggingface_hub import from_pretrained_keras |
| | |
| | model = from_pretrained_keras("hadilq/dragon-notdragon") |
| | |
| | img = keras.preprocessing.image.load_img(filename, target_size=(224, 224)) |
| | x = keras.preprocessing.image.img_to_array(img) |
| | x = np.expand_dims(x, axis=0) |
| | x = keras.applications.vgg16.preprocess_input(x) |
| | prediction = model.predict(x) |
| | print("model:", filename, "dragon" if prediction[0][0] >= 0.99 else "notdragon") |
| | ``` |
| |
|
| | Additionally, you can check https://replicate.com/hadilq/dragon-notdragon to play around. |
| |
|
| | ## Training procedure |
| | I trained it in Google colab, where you can find the original code in `training` directory. |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| |
|
| | | Hyperparameters | Value | |
| | | :-- | :-- | |
| | | name | Adam | |
| | | weight_decay | None | |
| | | clipnorm | None | |
| | | global_clipnorm | None | |
| | | clipvalue | None | |
| | | use_ema | False | |
| | | ema_momentum | 0.99 | |
| | | ema_overwrite_frequency | None | |
| | | jit_compile | True | |
| | | is_legacy_optimizer | False | |
| | | learning_rate | 9.999999747378752e-05 | |
| | | beta_1 | 0.9 | |
| | | beta_2 | 0.999 | |
| | | epsilon | 1e-07 | |
| | | amsgrad | False | |
| | | training_precision | float32 | |
| | |
| | |
| | ## Model Plot |
| | |
| | <details> |
| | <summary>View Model Plot</summary> |
| | |
| |  |
| | |
| | </details> |
| | |
