DOCCA0 / chef_hat_detection

厨师帽子检测

YoloV3 实现厨师帽检测

实现：tensorflow2.1.0，感谢yolov3框架：https://github.com/zzh8829/yolov3-tf2 注意：建议环境ubuntu,cuda10.1,以及相匹配版本的cudnn

安装Nvidia 驱动程序 (GPU)

首先要确保显卡是NVIDIA,否则会有不知名的错误

# Ubuntu 18.04
sudo apt-add-repository -r ppa:graphics-drivers/ppa
sudo apt install nvidia-driver-430
# Windows/Other
https://www.nvidia.com/Download/index.aspx

转换权重文件

yolov3

wget https://pjreddie.com/media/files/yolov3.weights -O data/yolov3.weights
python convert.py

yolov3-tiny

wget https://pjreddie.com/media/files/yolov3-tiny.weights -O data/yolov3-tiny.weights
python convert.py --weights ./data/yolov3-tiny.weights --output ./checkpoints/yolov3-tiny.tf --tiny

开始识别（输出图片）

下列所有的命令行参数，建议直接在.py文件中修改

# yolov3
python detect.py --image （要训练的图片地址+图片名）

# yolov3-tiny
python detect.py --weights ./checkpoints/yolov3-tiny.tf --tiny --image （要训练的图片地址+图片名）

# webcam（流）
python detect_video.py --video 0

开始识别（输出视频）

python detect_video.py --video path_to_file.mp4 

训练自己的数据集

请首先移步至docs/training_voc.md 示例：（同样，建议直接修改.py里面的命令行参数）

python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 100 --mode eager_tf --transfer fine_tune

python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 100 --mode fit --transfer none

python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 100 --mode fit --transfer no_output

python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 10 --mode eager_fit --transfer fine_tune --weights ./checkpoints/yolov3-tiny.tf --tiny

##以下为框架作者的补充说明： The inputs are preprocessed images (see dataset.transform_iamges)

outputs are

yolo_nms_0: bounding boxes
yolo_nms_1: scores
yolo_nms_2: classes
yolo_nms_3: numbers of valid detections

Macbook Pro 13 (2.7GHz i5)

Detection	416x416	320x320	608x608
YoloV3	1000ms	500ms	1546ms
YoloV3-Tiny	100ms	58ms	208ms

Desktop PC (GTX 970)

Detection	416x416	320x320	608x608
YoloV3	74ms	57ms	129ms
YoloV3-Tiny	18ms	15ms	28ms

AWS g3.4xlarge (Tesla M60)

Detection	416x416	320x320	608x608
YoloV3	66ms	50ms	123ms
YoloV3-Tiny	15ms	10ms	24ms

RTX 2070 (credit to @AnaRhisT94)

Detection	416x416
YoloV3 predict_on_batch	29-32ms
YoloV3 predict_on_batch + TensorRT	22-28ms

Darknet version of YoloV3 at 416x416 takes 29ms on Titan X. Considering Titan X has about double the benchmark of Tesla M60, Performance-wise this implementation is pretty comparable.

Implementation Details

Eager execution

Great addition for existing TensorFlow experts. Not very easy to use without some intermediate understanding of TensorFlow graphs. It is annoying when you accidentally use incompatible features like tensor.shape[0] or some sort of python control flow that works fine in eager mode, but totally breaks down when you try to compile the model to graph.

model(x) vs. model.predict(x)

When calling model(x) directly, we are executing the graph in eager mode. For model.predict, tf actually compiles the graph on the first run and then execute in graph mode. So if you are only running the model once, model(x) is faster since there is no compilation needed. Otherwise, model.predict or using exported SavedModel graph is much faster (by 2x). For non real-time usage, model.predict_on_batch is even faster as tested by @AnaRhisT94)

GradientTape

Extremely useful for debugging purpose, you can set breakpoints anywhere. You can compile all the keras fitting functionalities with gradient tape using the run_eagerly argument in model.compile. From my limited testing, all training methods including GradientTape, keras.fit, eager or not yeilds similar performance. But graph mode is still preferred since it's a tiny bit more efficient.

@tf.function

@tf.function is very cool. It's like an in-between version of eager and graph. You can step through the function by disabling tf.function and then gain performance when you enable it in production. Important note, you should not pass any non-tensor parameter to @tf.function, it will cause re-compilation on every call. I am not sure whats the best way other than using globals.

absl.py (abseil)

Absolutely amazing. If you don't know already, absl.py is officially used by internal projects at Google. It standardizes application interface for Python and many other languages. After using it within Google, I was so excited to hear abseil going open source. It includes many decades of best practices learned from creating large size scalable applications. I literally have nothing bad to say about it, strongly recommend absl.py to everybody.

Loading pre-trained Darknet weights

very hard with pure functional API because the layer ordering is different in tf.keras and darknet. The clean solution here is creating sub-models in keras. Keras is not able to save nested model in h5 format properly, TF Checkpoint is recommended since its offically supported by TensorFlow.

tf.keras.layers.BatchNormalization

It doesn't work very well for transfer learning. There are many articles and github issues all over the internet. I used a simple hack to make it work nicer on transfer learning with small batches.

What is the output of transform_targets ???

I know it's very confusion but the output is tuple of shape

(
  [N, 13, 13, 3, 6],
  [N, 26, 26, 3, 6],
  [N, 52, 52, 3, 6]
)

where N is the number of labels in batch and the last dimension "6" represents [x, y, w, h, obj, class] of the bounding boxes.

IOU and Score Threshold

the default threshold is 0.5 for both IOU and score, you can adjust them according to your need by setting --yolo_iou_threshold and --yolo_score_threshold flags

Maximum number of boxes

By default there can be maximum 100 bounding boxes per image, if for some reason you would like to have more boxes you can use the --yolo_max_boxes flag.

NAN Loss / Training Failed / Doesn't Converge

Many people including me have succeeded in training, so the code definitely works @LongxingTan in zzh8829/yolov3-tf2#128 provided some of his insights summarized here:

1. For nan loss, try to make learning rate smaller
2. Double check the format of your input data. Data input labelled by vott and labelImg is different. so make sure the input box is the right, and check carefully the format is x1/width,y1/height,x2/width,y2/height and NOT x1,y1,x2,y2, or x,y,w,h

Make sure to visualize your custom dataset using this tool

python tools/visualize_dataset.py --classes=./data/voc2012.names

It will output one random image from your dataset with label to output.jpg Training definitely won't work if the rendered label doesn't look correct

Command Line Args Reference

convert.py:
  --output: path to output
    (default: './checkpoints/yolov3.tf')
  --[no]tiny: yolov3 or yolov3-tiny
    (default: 'false')
  --weights: path to weights file
    (default: './data/yolov3.weights')
  --num_classes: number of classes in the model
    (default: '80')
    (an integer)

detect.py:
  --classes: path to classes file
    (default: './data/coco.names')
  --image: path to input image
    (default: './data/girl.png')
  --output: path to output image
    (default: './output.jpg')
  --[no]tiny: yolov3 or yolov3-tiny
    (default: 'false')
  --weights: path to weights file
    (default: './checkpoints/yolov3.tf')
  --num_classes: number of classes in the model
    (default: '80')
    (an integer)

detect_video.py:
  --classes: path to classes file
    (default: './data/coco.names')
  --video: path to input video (use 0 for cam)
    (default: './data/video.mp4')
  --output: path to output video (remember to set right codec for given format. e.g. XVID for .avi)
    (default: None)
  --output_format: codec used in VideoWriter when saving video to file
    (default: 'XVID)
  --[no]tiny: yolov3 or yolov3-tiny
    (default: 'false')
  --weights: path to weights file
    (default: './checkpoints/yolov3.tf')
  --num_classes: number of classes in the model
    (default: '80')
    (an integer)

train.py:
  --batch_size: batch size
    (default: '8')
    (an integer)
  --classes: path to classes file
    (default: './data/coco.names')
  --dataset: path to dataset
    (default: '')
  --epochs: number of epochs
    (default: '2')
    (an integer)
  --learning_rate: learning rate
    (default: '0.001')
    (a number)
  --mode: <fit|eager_fit|eager_tf>: fit: model.fit, eager_fit: model.fit(run_eagerly=True), eager_tf: custom GradientTape
    (default: 'fit')
  --num_classes: number of classes in the model
    (default: '80')
    (an integer)
  --size: image size
    (default: '416')
    (an integer)
  --[no]tiny: yolov3 or yolov3-tiny
    (default: 'false')
  --transfer: <none|darknet|no_output|frozen|fine_tune>: none: Training from scratch, darknet: Transfer darknet, no_output: Transfer all but output, frozen: Transfer and freeze all,
    fine_tune: Transfer all and freeze darknet only
    (default: 'none')
  --val_dataset: path to validation dataset
    (default: '')
  --weights: path to weights file
    (default: './checkpoints/yolov3.tf')

Change Log

October 1, 2019

• Updated to Tensorflow to v2.0.0 Release

References

It is pretty much impossible to implement this from the yolov3 paper alone. I had to reference the official (very hard to understand) and many un-official (many minor errors) repos to piece together the complete picture.

• https://github.com/pjreddie/darknet
  • official yolov3 implementation
• https://github.com/AlexeyAB
  • explinations of parameters
• https://github.com/qqwweee/keras-yolo3
  • models
  • loss functions
• https://github.com/YunYang1994/tensorflow-yolov3
  • data transformations
  • loss functions
• https://github.com/ayooshkathuria/pytorch-yolo-v3
  • models
• https://github.com/broadinstitute/keras-resnet
  • batch normalization fix