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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](https://huggingface.co/datasets/pdDuduzin/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`. |