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QUICK_STARTED.md

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Quick Start

This tutorial fine-tunes a tiny dataset by pretrained detection model for users to get a model and learn PaddleDetection quickly. The model can be trained in around 20min with good performance.

Data Preparation

Dataset refers to Kaggle, which contains 240 images in train dataset and 60 images in test dataset. Data categories are apple, orange and banana. Download here and uncompress the dataset after download, script for data preparation is located at download_fruit.py. Command is as follows:

export PYTHONPATH=$PYTHONPATH:.
python dataset/fruit/download_fruit.py
  • Note: before started, run the following command and specifiy the GPU
export PYTHONPATH=$PYTHONPATH:.
export CUDA_VISIBLE_DEVICES=0

Training:

python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml \
                        --use_tb=True \
                        --tb_log_dir=tb_fruit_dir/scalar \
                        --eval

Use yolov3_mobilenet_v1 to fine-tune the model from COCO dataset. Meanwhile, loss and mAP can be observed on tensorboard.

tensorboard --logdir tb_fruit_dir/scalar/ --host <host_IP> --port <port_num>

Result on tensorboard is shown below:

Model can be downloaded here

Evaluation:

python -u tools/eval.py -c configs/yolov3_mobilenet_v1_fruit.yml

Inference:

python -u tools/infer.py -c configs/yolov3_mobilenet_v1_fruit.yml \
                         -o weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_fruit.tar \
                         --infer_img=demo/orange_71.jpg

Inference images are shown below:

For detailed infomation of training and evalution, please refer to GETTING_STARTED.md.