README.md

metadata

datasets:
  - pdDuduzin/ESP32-CAM_Autonomous_Line-Following_Car_Dataset
tags:
  - tensorflow-lite
  - esp32
  - computer-vision
  - line-following
  - embedded-ml
license: apache-2.0
language:
  - en
  - pt

ESP32-CAM Autonomous Line-Following Car Model

Quantized steering model used by an autonomous line-following car built with an ESP32-CAM and an ESP32 DevKit.

Model contract

• Input: 96 x 96 x 1 grayscale image.
• Output: one steering value from -1 (maximum left) to +1 (maximum right).
• Task: supervised regression by behavioral cloning.
• Deployment format: fully quantized INT8 TensorFlow Lite Micro model.
• Dataset: ESP32-CAM Autonomous Line-Following Car Dataset.

Files

• modelo_linha.tflite: quantized deployment model.
• modelo_linha.h: C header embedded in the ESP32-CAM firmware.
• modelo_linha.keras: original Keras model.
• training_manifest.json: training configuration, session split and hashes.
• conversion_manifest.json: quantization configuration, validation metrics and hashes.
• avaliacao_val.json: validation report.
• historico_treino.png and precisao_val.png: training and validation plots.

Validation results

For the INT8 model on the validation split:

• MAE: 0.2585
• MSE: 0.1145
• R²: 0.5372
• Curve direction accuracy: 95.81%

These offline metrics do not guarantee stable driving. Performance is sensitive to lighting, reflections, track appearance, camera position and conditions that differ from the training data.

Deployment

Copy modelo_linha.h to firmware/esp32cam_autonomous/modelo_linha.h, compile the autonomous firmware and upload it to the ESP32-CAM. The header in this release has SHA-256 f835b35dae58bbae79324b01cc249774e020db102483add12ce391a6d3fae751.