This is our implementation of training a helmet detection with the original author's modified YOLOv3,,WSHD provide the dataset used for both safety helmet wearing and human head detection. It includes 7581 images with 9044 human safety helmet wearing objects(positive) and 111514 normal head objects(not wearing or negative). The positive objects got from goolge or baidu, and we manually labeld with LabelImg. Some of negative objects got from SCUT-HEAD. We fixed some bugs for original SCUT-HEAD and make the data can be directly loaded as normal Pascal VOC format. The key features of this repo are:
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Efficient tf.data pipeline
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It is a implementation of YOLOv3 in pure TensorFlow.
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Weights converter (converting pretrained darknet weights on COCO dataset to TensorFlow checkpoint.)
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Extremely fast GPU non maximum supression.
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Full training and evaluation pipeline.
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Kmeans algorithm to select prior anchor boxes.
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Also we provide some pretrained models with MXNet Gluon CV.
If this article has a little help for you, please lift a finger, give this code a small Star, let me share more content next time, and continue to improve this branch! Thank you very much!
如果本篇对你有一点点帮助，请举手之劳，给本代码一个小小的Star，让我下次分享更多内容，以及持续完善该环节！谢谢！
https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset/blob/master/image/7_result.jpg
Python version: 2 or 3
Packages:
- tensorflow >= 1.8.0 (theoretically any version that supports tf.data is ok)
- opencv-python
- tqdm
The pretrained darknet weights file can be downloaded here. Place this weights file under directory ./data/darknet_weights/ and then run:
python convert_weight.pyThen the converted TensorFlow checkpoint file will be saved to ./data/darknet_weights/ directory.
You can also download the converted TensorFlow checkpoint file by me via [Google Drive link] or [Github Release] and then place it to the same directory.
There are some demo images and videos under the ./data/demo_data/. You can run the demo by:
Single image test demo:
python test_single_image.py ./data/demo_data/gongdi01.jpgVideo test demo:
python video_test.py ./data/demo_data/video01.mp4Some results:
Compare the kite detection results with TensorFlow's offical API result here.
(The kite detection result is under input image resolution 1344x896)
How fast is the inference speed? With images scaled to 416*416:
| Backbone | GPU | Time(ms) |
|---|---|---|
| Darknet-53 (paper) | Titan X | 29 |
| Darknet-53 (my impl.) | Titan XP | ~23 |
why is it so fast? Check the ImageNet classification result comparision from the paper:
For better understanding of the model architecture, you can refer to the following picture. With great thanks to Levio for your excellent work!
(1) annotation file
Generate train.txt/val.txt/test.txt files under ./data/my_data/ directory. One line for one image, in the format like image_index image_absolute_path img_width img_height box_1 box_2 ... box_n. Box_x format: label_index x_min y_min x_max y_max. (The origin of coordinates is at the left top corner, left top => (xmin, ymin), right bottom => (xmax, ymax).) image_index is the line index which starts from zero. label_index is in range [0, class_num - 1].
For example:
0 xxx/xxx/a.jpg 1920 1080 0 453 369 473 391 1 588 245 608 268
1 xxx/xxx/b.jpg 1920 1080 1 466 403 485 422 2 793 300 809 320
...
Since so many users report to use tools like LabelImg to generate xml format annotations, I add one demo script on VOC dataset to do the convertion. Check the misc/parse_voc_xml.py file for more details.
(2) class_names file:
Generate the data.names file under ./data/my_data/ directory. Each line represents a class name.
For example:
hat
head
The COCO dataset class names file is placed at ./data/coco.names.
(3) prior anchor file:
Using the kmeans algorithm to get the prior anchors:
python get_kmeans.py
Then you will get 9 anchors and the average IoU. Save the anchors to a txt file.
The COCO dataset anchors offered by YOLO's author is placed at ./data/yolo_anchors.txt, you can use that one too.
The yolo anchors computed by the kmeans script is on the resized image scale. The default resize method is the letterbox resize, i.e., keep the original aspect ratio in the resized image.
Using train.py. The hyper-parameters and the corresponding annotations can be found in args.py:
CUDA_VISIBLE_DEVICES=GPU_ID python train.pyCheck the args.py for more details. You should set the parameters yourself in your own specific task.
Using eval.py to evaluate the validation or test dataset. The parameters are as following:
$ python eval.py -h
usage: eval.py [-h] [--eval_file EVAL_FILE]
[--restore_path RESTORE_PATH]
[--anchor_path ANCHOR_PATH]
[--class_name_path CLASS_NAME_PATH]
[--batch_size BATCH_SIZE]
[--img_size [IMG_SIZE [IMG_SIZE ...]]]
[--num_threads NUM_THREADS]
[--prefetech_buffer PREFETECH_BUFFER]
[--nms_threshold NMS_THRESHOLD]
[--score_threshold SCORE_THRESHOLD]
[--nms_topk NMS_TOPK]Check the eval.py for more details. You should set the parameters yourself.
You will get the loss, recall, precision, average precision and mAP metrics results.
For higher mAP, you should set score_threshold to a small number.
Here are some training tricks in my experiment:
(1) Apply the two-stage training strategy or the one-stage training strategy:
Two-stage training:
First stage: Restore darknet53_body part weights from COCO checkpoints, train the yolov3_head with big learning rate like 1e-3 until the loss reaches to a low level.
Second stage: Restore the weights from the first stage, then train the whole model with small learning rate like 1e-4 or smaller. At this stage remember to restore the optimizer parameters if you use optimizers like adam.
One-stage training:
Just restore the whole weight file except the last three convolution layers (Conv_6, Conv_14, Conv_22). In this condition, be careful about the possible nan loss value.
(2) I've included many useful training strategies in args.py:
- Cosine decay of lr (SGDR)
- Multi-scale training
- Label smoothing
- Mix up data augmentation
- Focal loss
These are all good strategies but it does not mean they will definitely improve the performance. You should choose the appropriate strategies for your own task.
This paper from gluon-cv has proved that data augmentation is critical to YOLO v3, which is completely in consistent with my own experiments. Some data augmentation strategies that seems reasonable may lead to poor performance. For example, after introducing random color jittering, the mAP on my own dataset drops heavily. Thus I hope you pay extra attention to the data augmentation.
(4) Loss nan? Setting a bigger warm_up_epoch number or smaller learning rate and try several more times. If you fine-tune the whole model, using adam may cause nan value sometimes. You can try choosing momentum optimizer.
I did a quick train on the VOC dataset. The params I used in my experiments are included under misc/experiments_on_voc/ folder for your reference. The train dataset is the VOC 2007 + 2012 trainval set, and the test dataset is the VOC 2007 test set.
Finally with the 416*416 input image, I got a 87.54% test mAP (not using the 07 metric). No hard-try fine-tuning. You should get the similar or better results.
My pretrained weights on VOC dataset can be downloaded here.
[ ] Multi-GPUs with sync batch norm.
[ ] Maybe tf 2.0 ?
I referred to many fantastic repos during the implementation:
eriklindernoren/PyTorch-YOLOv3
If this article has a little help for you, please lift a finger, give this code a small Star, let me share more content next time, and continue to improve this branch! Thank you very much!