| --- |
| 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']) |
