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YOLOv1 with Tensorflow 2

tf-v2.5.0

For ease of implementation, i have not implemented exactly the same as paper.
The things presented below are implemented differently from the paper.

  • Backbone network(Used Xception instead of network mentioned in the paper.)

  • Learning rate schedule(Used tf.keras.optimizers.schedules.ExponentialDecay)

  • Data augmentations

  • Hyper parameters

  • And so on ...



Preview

Tensorboard


Inference Visualization



Build Environment with Docker

Build Docker Image

docker build -t ${ImageName}:${ImageTag} .

Create a Container

  • Example
docker run -d -it --gpus all --shm-size=${ShmSize} ${ImageName}:${ImageTag} /bin/bash



Training Dataset: Pascal VOC Dataset (Link)

Pascal VOC Dataset with TFDS

Number of Images

Train Validation Test
Pascal VOC 2007 2501 2510 4952 (Used Validation)
Pascal VOC 2012 5717 5823 10991 (No labels)
  • Training Set: VOC2007 trainval + VOC2012 trainval (Total: 16551)
  • Validation Set: VOC2007 test (Total: 4952)



Pretrained PB File

Trained with default values of this repo. (Total Epoch: 105)

pb file is uploaded as tar.gz. So, you have to decompress this file like below.

tar -zxvf yolo_voc_448x448.tar.gz

If you want to inference with this pb file, infer to inference_tutorial.ipynb


Performance

Evaluation with VOC2007 test

class name AP
dog 0.7464
pottedplant 0.2250
car 0.5021
person 0.4482
tvmonitor 0.5213
diningtable 0.4564
bicycle 0.5927
chair 0.2041
motorbike 0.5595
sofa 0.4801
bus 0.6215
boat 0.3274
horse 0.7049
aeroplane 0.5872
sheep 0.4223
bottle 0.1312
train 0.7917
cat 0.8044
bird 0.4824
cow 0.4548
mAP 0.5032

Inference Speed

  • GPU(GeForce RTX 3090): About 8 FPS
  • CPU(AMD Ryzen 5 5600X 6-Core Processor): About 4 FPS



Training Script

Path: ./voc_scripts/train_voc.py

python train_voc.py

Options

Default option values are ./configs/configs.py.
If the options are given, the default config values are overridden.

  • --epochs: Number of training epochs
  • --init_lr: Initial learning rate
  • --lr_decay_rate: Learning rate decay rate
  • --lr_decay_steps: Learning rate decay steps
  • --batch_size: Training batch size
  • --val_step: Validation interval during training
  • --tb_img_max_outputs : Number of visualized prediction images in tensorboard
  • --train_ds_sample_ratio: Training dataset sampling ratio
  • --val_ds_sample_ratio: Validation dataset sampling ratio

Evaluation Script

Path: ./voc_scripts/eval_voc.py

Evaluation pretrained model with VOC2007 Test Dataset

python eval_voc.py

Options

  • --batch_size: Evaluation batch size (Default: batch_size of ./configs/configs.py.)
  • --pb_dir: Save pb directory path (Default: ./ckpts/voc_ckpts/yolo_voc_448x448)



Citation

You Only Look Once: Unified, Real-Time Object Detection <arxiv link>

@misc{redmon2016look,
      title={You Only Look Once: Unified, Real-Time Object Detection}, 
      author={Joseph Redmon and Santosh Divvala and Ross Girshick and Ali Farhadi},
      year={2016},
      eprint={1506.02640},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}