---
language:
- en
tags:
- tflite
- deep-learning
- mobile
license: apache-2.0
datasets:
- RDD2022
metrics:
- precision
model-index:
- name: POT-YOLO
  results:
  - task:
      type: Object-Detection
      name: Object Detection
    dataset:
      name: RDD2022_Customized
      type: Object-Detection
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.62
library_name: transformers
pipeline_tag: object-detection
---

# Your Model Name

## Model description
This model is a TFLite version of a [model architecture] trained to perform [task], such as [image classification, object detection, etc.]. It has been optimized for mobile and edge devices, ensuring efficient performance while maintaining accuracy.

## Model architecture
The model is based on [model architecture] and has been converted to TFLite for deployment on mobile and embedded devices. It includes optimizations like quantization to reduce model size and improve inference speed.

## Intended uses & limitations
This model is intended for [use cases, e.g., real-time image classification on mobile devices]. It may not perform well on [limitations, e.g., images with poor lighting or low resolution].

## Training data
The model was trained on the [your dataset name] dataset, which consists of [describe the dataset, e.g., 10,000 labeled images across 10 categories].

## Evaluation
The model was evaluated on the [your dataset name] test set, achieving an accuracy of [accuracy value]. Evaluation metrics include accuracy and [any other relevant metrics].

## How to use
You can use this model in your application by loading the TFLite model and running inference using TensorFlow Lite's interpreter.

```python
import tensorflow as tf

# Load the TFLite model and allocate tensors
interpreter = tf.lite.Interpreter(model_path="path/to/PotYOLO_int8.tflite")
interpreter.allocate_tensors()

# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Prepare input data
input_data = ... # Preprocess your input data

# Run inference
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()

# Get the result
output_data = interpreter.get_tensor(output_details[0]['index'])