Are you tired of dealing with TensorFlow 1.x and in search of a modern alternative? Look no further! In this guide, we will walk through the steps to clone and run a YOLOv3 implementation using TensorFlow 2.0. Plus, we’ll explore how to train your own dataset effectively.

Part 1: Quick Start

Step 1: Clone the Repository

Begin your journey by cloning the YOLOv3 repository:

git clone https://github.com/YunYang1994/tensorflow-yolov3.git

Step 2: Install Dependencies

You will need to install certain dependencies to work with the code:

cd tensorflow-yolov3
pip install -r .docs/requirements.txt

Step 3: Export COCO Weights

Next, you must export the loaded COCO weights as a TensorFlow checkpoint:

cd checkpoint
wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz
tar -xvf yolov3_coco.tar.gz
cd ..
python convert_weight.py
python freeze_graph.py

This will generate some .pb files in the root directory. To run the demo script:

python image_demo.py
python video_demo.py # if using camera, set video_path = 0

Step 4: View Output

You should see the object detection results appear from either an image or live video feed.

Part 2: Train Your Own Dataset

To train the YOLOv3 model on your own dataset, you will need two critical files:

• dataset.txt: Contains your image paths and bounding box coordinates.
• class.names: Defines the classes your model will recognize.

2.1 Training on VOC Dataset

First, download the VOC PASCAL trainval and test data:

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

After downloading, extract all tar files into one directory and rename them accordingly. Follow the directory structure shown above, and run the following command:

python scripts/voc_annotation.py --data_path homeyang/test/VOC

Update your .core/config.py with the paths to your classes and datasets. Here’s a sample of the configuration:

__C.YOLO.CLASSES = .data/classes/voc.names
__C.TRAIN.ANNOT_PATH = .data/dataset/voc_train.txt
__C.TEST.ANNOT_PATH = .data/dataset/voc_test.txt

Training Methods

There are two ways to start your training:

• Train from Scratch:

python train.py
tensorboard --logdir .data

• Train from COCO Weights (recommended):

cd checkpoint
wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz
tar -xvf yolov3_coco.tar.gz
cd ..
python convert_weight.py --train_from_coco
python train.py

2.2 Evaluate on VOC Dataset

Finally, after training, you can evaluate the model’s performance:

python evaluate.py
cd mAP
python main.py -n

Part 3: Additional Implementations

If you wish to explore further, here are some alternatives:

• YOLOv3 with TensorFlow
• Stronger-yolo
• Implementing YOLO v3 in Tensorflow (TF-Slim)
• YOLOv3_TensorFlow
• Object Detection using YOLOv2 on Pascal VOC2012
• Understanding YOLO

Troubleshooting

Even the most seasoned programmers can hit roadblocks. Here are a few troubleshooting tips:

• Ensure all dependencies are installed correctly.
• Check for any errors related to file paths when loading the dataset or weights.
• If you face issues with TensorBoard not displaying logs, verify your log directory.
• If the demo scripts don’t run as expected, ensure that the image or camera path is correctly set.

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