---
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>

![Model Image](./model.png)

</details>