---
library_name: tflite
pipeline_tag: image-classification
tags:
- image-classification
- tflite
- tensorflow
- gender-classification
base_model: rizvandwiki/gender-classification-2
---

# Gender classification (TensorFlow / TFLite)

TensorFlow and TFLite conversion of
[rizvandwiki/gender-classification-2](https://huggingface.co/rizvandwiki/gender-classification-2)
(ViT). Please check the original model card for license and training details.

## Files
- `gender_model.tflite` : mobile model (float16)
- `tf_model.h5` : TensorFlow (Keras) weights
- `labels.txt` : 0 = female, 1 = male

## Input / preprocessing
- Shape: `[1, 3, 224, 224]` (channels first, RGB)
- Normalize: `(pixel / 255 - 0.5) / 0.5`
- Detect and crop the face before running the model.
- Output: 2 logits -> apply softmax.

Note: converted with SELECT_TF_OPS, so on Android add
`org.tensorflow:tensorflow-lite-select-tf-ops`.

## Python example
```python
import numpy as np, tensorflow as tf
from huggingface_hub import hf_hub_download

path = hf_hub_download("SynthAIzer/gender-classification-tflite", "gender_model.tflite")
interp = tf.lite.Interpreter(model_path=path)
interp.allocate_tensors()
```