YOLOv4-Tiny Pothole Detection Pipeline

This guide details the steps to train a YOLOv4-tiny model for pothole detection, convert it to TensorFlow Lite format, and test it using sample images.

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1. Load an Existing Model

Start by creating a new notebook in Google Colab and mount your Google Drive:

from google.colab import drive
drive.mount('/content/drive')

Download the pre-trained YOLOv4-tiny weights:

!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29

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2. Dataset Preparation

Download the pothole dataset from Roboflow in YOLO Darknet format.

• Extract .zip into a folder named Images.
• Delete any README and .labels files from train, valid, and test folders.
• Zip the cleaned Images folder as obj.zip.

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3. Setup Required Files

Create a folder yolov4-tiny in your Google Drive, then a subfolder training.

Upload these to yolov4-tiny:

• obj.zip
• yolov4-tiny-custom.cfg (with modified training parameters)
• obj.names
• obj.data
• process.py

Modify yolov4-tiny-custom.cfg:

• batch=64, subdivisions=16, width=416, height=416
• max_batches=6000, steps=4800,5400
• Set correct filters and classes

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4. Setup Darknet

Clone and configure Darknet:

!git clone https://github.com/AlexeyAB/darknet
%cd darknet
!sed -i 's/OPENCV=0/OPENCV=1/' Makefile
!sed -i 's/GPU=0/GPU=1/' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/' Makefile
!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile
!make

Link Drive:

!ln -s /content/drive/My\ Drive/ /mydrive

Prepare folders and copy config:

%cd data/
!find -maxdepth 1 -type f -exec rm -rf {{}} \;
%cd ..
%rm -rf cfg/
%mkdir cfg
!cp /mydrive/yolov4-tiny/obj.zip ../
!unzip ../obj.zip -d data/
!cp /mydrive/yolov4-tiny/yolov4-tiny-custom.cfg ./cfg
!cp /mydrive/yolov4-tiny/obj.names ./data
!cp /mydrive/yolov4-tiny/obj.data  ./data
!cp /mydrive/yolov4-tiny/process.py ./
!python process.py

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5. Train the Model

Run training with transfer learning:

!./darknet detector train data/obj.data cfg/yolov4-tiny-custom.cfg yolov4-tiny.conv.29 -dont_show -map

Test the detector:

!wget -O /mydrive/yolov4-tiny/ph.jpg https://raw.githubusercontent.com/SanaulMalik/SherlockHoles/master/images/ph.jpg
!./darknet detector test data/obj.data cfg/yolov4-tiny-custom.cfg /mydrive/yolov4-tiny/training/yolov4-tiny-custom_best.weights /mydrive/yolov4-tiny/ph.jpg -thresh 0.3

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6. Convert to .pb Format

Switch runtime from GPU to None.

Clone converter repo:

!git clone https://github.com/hunglc007/tensorflow-yolov4-tflite.git

Edit core/config.py:

__C.YOLO.CLASSES = "mydrive/yolov4-tiny/obj.names"

Convert weights:

!python save_model.py --weights /mydrive/yolov4-tiny/training/yolov4-tiny-custom_best.weights --output /mydrive/yolov4-tiny/yolov4-tiny-pb --input_size 416 --model yolov4 --framework tflite --tiny

Verify:

import tensorflow as tf
model = tf.keras.models.load_model("/mydrive/yolov4-tiny/yolov4-tiny-pb")
model.summary()

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7. Convert to .tflite

Edit convert_tflite.py:

converter.experimental_enable_resource_variables = True

Convert:

!python convert_tflite.py --weights /mydrive/yolov4-tiny/yolov4-tiny-pb --output /mydrive/yolov4-tiny/model.tflite --quantize_mode float16

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8. Test the TFLite Model

!git clone https://github.com/SanaulMalik/SherlockHoles
%cd /mydrive/yolov4-tiny
!cp /content/SherlockHoles/executables/detect_tflite.py ./
!cp /content/SherlockHoles/images/ph-2.jpg ./ph-2.jpg
!python detect_tflite.py --weights model.tflite --size 416 --model yolov4 --image ph-2.jpg --framework tflite --tiny

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✅ Output

The output will be saved as result.png in your current working directory (i.e. yolov4-tiny folder).

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🧵 Acknowledgements

• AlexeyAB/darknet
• hunglc007/tensorflow-yolov4-tflite
• Roboflow Pothole Dataset