---
license: apache-2.0
language:
- en
- yo
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- Yoruba
- tradition
- cap
- fila
- gobi
- Nigeria
- Oodua
- gods
---
# Fila Yoruba Detector

## Model Description
This model is a binary classification model that detects whether an image contains a traditional Yoruba people's **fila** (hat) or not. It was trained on a small set of images to recognize two classes: **fila** and **not-fila**.

## Intended Use
This model is designed to classify images of traditional Yoruba attire, specifically detecting the presence of a **fila**. It can be used in applications where recognizing this cultural item is important, such as in cultural heritage analysis or fashion recognition.

## Training Data
The model was trained on a dataset of images containing two classes:
- **fila**: Images that contain the traditional Yoruba hat.
- **not-fila**: Images that do not contain the traditional Yoruba hat.

The training data consisted of a limited number of images and may not generalize well to other image sets outside of the Yoruba context. You can test the model here: [here](https://dolaposalim-mori-fila.hf.space/?__theme=system&deep_link=fdWZ0JlkvqU)
## Evaluation Metrics
The model's performance was evaluated using standard classification metrics:
- **Accuracy**: 85%
- **Precision**: 88%
- **Recall**: 80%

Note: These metrics may vary depending on the images it is tested on.

## Limitations
- The model was trained on a limited set of images, which might affect its generalization to other types of images.
- The model may have difficulty recognizing **fila** in images with poor lighting or obscured views of the hat.
- The performance may degrade if the model encounters images from other cultures or different types of headgear.

## Ethical Considerations
- Ensure that the model is used in contexts that respect cultural diversity and avoid reinforcing stereotypes.
- The model might not perform equally well across all demographic groups, and care should be taken to avoid misuse in sensitive contexts.

## How to Use
To use this model, you can load it from Hugging Face using the following code:

```python
from huggingface_hub import hf_hub_download
import tensorflow as tf

# Download and load the model
model_path = hf_hub_download(repo_id="dolaposalim/model-name", filename="filadentification.h5")
model = tf.keras.models.load_model(model_path)

# Predict function
def predict(image):
    # Preprocess the image and make predictions
    image = image.resize((256, 256))  # Resize to model input size
    image = np.array(image) / 255.0   # Normalize
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    prediction = model.predict(image)
    return prediction```