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4_image_detection.pdf
- 1. 國立臺北護理健康大學 NTUNHS Image Detection Orozco Hsu 2022-05-09 1
- 2. About me • Education • NCU (MIS)、NCCU (CS) • Work Experience • Telecom big data Innovation • AI projects • Retail marketing technology • User Group • TW Spark User Group • TW Hadoop User Group • Taiwan Data Engineer Association Director • Research • Big Data/ ML/ AIOT/ AI Columnist 2
- 3. Tutorial Content 3 Useful dataset introduction Homework Image Detection (YOLOX) Labeling images and create your own model
- 4. Code • Download code • https://github.com/orozcohsu/ntunhs_2022_01.git • Folder/file • 20220509_inter_master/run.ipynb • 20220509_inter_master/split_voc.py 4
- 5. Code 5 Click button Open it with Colab Copy it to your google drive Check your google drive
- 8. Object Detection Milestones 8 Ref: https://www.researchgate.net/figure/A-road-map-of-object-detection-Milestone-detectors-in-this-figure-VJ-Det-10-11-HOG_fig2_333077580
- 9. 9 Input Image Backbone Feature extraction from pre-train model (ImageNet); VGG16, ResNet, EfficientNet, CSPDarknet53 Neck Integrates feature map from each layers; Using SPP or FPN or PAN… Head Predicts bbox from those integrated feature map • One-stage: predicts whether there is a bbox in each grid-cell (dense) • Two-stage: predicts ROI only (sparse) https://medium.com/mdbros/%E4%BA%94%E5%88%86%E9%90%98%E8%AA%8D%E8%AD%98%E7%94%A8-yolo- %E5%81%9A%E8%A1%80%E6%B6%B2%E6%8A%B9%E7%89%87%E7%89%A9%E4%BB%B6%E5%81%B5%E6%B8%AC-object-detection- be3c77e13228
- 11. About YOLOX • YOLOX is a high-performance anchor-free YOLO • Exceeding YOLOV3, V4, V5 • With ONNX, TensorRT, OpenVINO 11 Jetson Nano + TensorRT Rasperry Pi + OpenVINO
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- 14. You Only Look Once • YOLO divides the feature-map into small grid-cells • Each grid is responsible for the detection of the targets at one time • Predicting the bounding box • Location • Confidence of the targets • Probability of all labels 14
- 15. 15 Union the same label grid-cells and bolden the edge of box Positive sample (bbox offset/ Regression) Negative sample (background) Ignore sample (not in the training labels)
- 16. 16 The length depends on dataset label X, Y : the central of grid-cell value W, H: the prediction of object value
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- 18. Anchor-Base/ Anchor-Free 18 Anchor-based: Provide some pre-defined boxes (large and small) Adjustment to scale the size of anchor for object prediction https://medium.com/%E8%BB%9F%E9%AB%94%E4%B9%8B%E 5%BF%83/cv-object-detection-1-anchor- free%E5%A4%A7%E7%88%86%E7%99%BC%E7%9A%842019%E 5%B9%B4-e3b4271cdf1a Anchor-free: Use FPN and focal loss https://medium.com/%E8%BB%9F%E9%AB%94%E4%B9%8B%E 5%BF%83/%E9%80%9A%E5%BE%80anchor- free%E7%9A%84%E7%9C%9F%E7%9B%B8-object- detection%E7%9A%84%E6%AD%A3%E8%B2%A0%E6%A8%A3% E6%9C%AC%E5%AE%9A%E7%BE%A9-83f2fe36167f https://arxiv.org/pdf/1708.02002.pdf
- 19. Feature Pyramid Network (FPN) 19 Union the feature maps for prediction https://arxiv.org/pdf/1612.03144.pdf
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- 21. Loss function • Classification loss • Localization loss (the loss of G.T. box and prediction box) • Confidence loss (object of the IOU box) • Total loss: • Classification loss + Localization loss + Confidence loss 21
- 22. Non-Maximum Suppression (NMS) 22 This is in the testing process (not in training process), because there is no G.T. bbox
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- 37. PASCAL VOC Datasets • PASCAL VOC • Pattern Analysis Statistical and Computational Learning (Visual Object Classes) • PASCAL VOC • The dataset of image detection challenge of between 2005 and 2012 • In total, there are 20 labels • PASCAL VOC 2007 • 9,963 images • 24,640 annotation • PASCAL VOC 2012 • 11,530 images • 27,450 annotation 37
- 38. PASCAL VOC Datasets • Person: person • Animal: bird, cat, cow, dog, horse, sheep • Vehicle: aero-plane, bicycle, boat, bus, car, motorbike, train • Indoor: bottle, chair, dining-table, potted-plant, sofa, tv 38 20 labels Rex: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html
- 39. MS COCO Datasets • MS COCO • Microsoft Common Object in Context 39 Ref: https://cocodataset.org/#home
- 40. Download LabelImg (For Windows) • LabelImg • Windows_v1.8.0 • Download: https://tzutalin.github.io/labelImg/ • Tutorial: https://tw.leaderg.com/article/index?sn=11159 40 Another annotation tool: http://www.jinglingbiaozhu.com/
- 41. LabelImg PACAL VOC • PASCAL VOC (xml) annotation format 41
- 42. LabelImg YOLO • YOLO annotation format 42
- 43. LabelImg • Define the label in windows_v1.8.0datapredefined_classes.txt • Open the LabelImg tool and go to 「Open Dir 」 • Zoom-in/out to scale your image and click 「Create RecBox 」 • Choose the label and 「Save 」 • Continue to label next image and click 「Next Image 」 43
- 44. LabelImg 44
- 45. LabelImg • Go to your label folder, and check those files (PASCAL VOC) • Double check the VOCdevkitVOC2007 45
- 46. YOLOX training • Run the run.ipynb and go to CoLab (CoLab.ipynb) • Make sure it is in GPU runtime 46
- 47. Config YOLOX • Modify the YOLOX/exps/example/yolox_voc/yolox_voc_s.py • Check YOLOX/exps/default for each proper depth and width 47 self.num_classes = 2 image_sets=[('2007', 'trainval')],
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- 49. Config YOLOX • Modify the YOLOX/yolox/data/datasets/voc_classes.py and YOLOX/yolox/data/datasets/coco_classes.py 49
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- 51. 51 Consider to reduce the max epoch in YOLOX/yolox/exp/yolox_base.py
- 52. Config YOLOX • Add executable path to train.py 52
- 53. YOLOX training • Training command 53 python tools/train.py -f exps/example/yolox_voc/yolox_voc_s.py -d 1 -b 16 --fp16 -o -c yolox_s.pth f: yolox configuration file fp16: Half-precision floating-point format, refer to https://en.wikipedia.org/wiki/Half-precision_floating-point_format d: define your GPU amont b: define your batch size (adjust it for memory usage) o: occupy GPU memory first for training c: network weight file
- 56. YOLOX output 56
- 57. YOLOX output 57
- 58. More • Deploy model to edge device for real-time detection • Edge device • Use acceleration device on edge device • TensorRT, OpenVINO • No acceleration device on edge device • https://medium.com/ching-i/yolo-fastest-ncnn-on-raspberry-pi-4- f44143b44e45 • https://chtseng.wordpress.com/2020/09/27/%E8%A6%BA%E5%BE %97yolo- tiny%E4%B8%8D%E5%A4%A0%E5%BF%AB%E5%97%8E%EF%BC% 9F%E8%A9%A6%E8%A9%A6yolo-fastest/ 58
- 59. More • YOLOX resume from epoch • https://chowdera.com/2021/11/20211119022033342w.html • YOLOX documentation • https://yolox.readthedocs.io/_/downloads/en/latest/pdf/ 59
- 60. Homework • Try to label other classes (more than 2) and rebuild your YOLOX model (at least 1,500 images or more for training and testing data) • https://blog.csdn.net/nan355655600/article/details/119519294 • If you want to use yolo label format • https://d246810g2000.medium.com/%E5%A6%82%E4%BD%95%E4%BD%BF %E7%94%A8%E8%87%AA%E5%B7%B1%E7%9A%84%E8%B3%87%E6%96%99 %E9%9B%86%E8%A8%93%E7%B7%B4-yolox-c02548734a48 60