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Object Detection with Tensorflow
This document provides an overview of object detection with TensorFlow. It introduces object detection and the state of deep learning approaches. It then describes the TensorFlow Object Detection API for building, training and deploying object detection models. It outlines the steps for preparing a dataset by collecting and annotating images and converting them to TFRecord format. Finally, it discusses configuring, training and evaluating models using the API.
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Object Detection with Tensorflow
- 1. Object Detection withTensorflow by Anatolii Shkurpylo, Software Developer
- 2. www.eliftech.com Agenda ▪ Intro ▪ Whatis Object Detection ▪ State of Object Detection ▪ Tensorflow Object Detection API ▪ Preparing Data ▪ Training & Evaluating ▪ Links
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- 5. www.eliftech.com What is ObjectDetection
- 6. www.eliftech.com Object detection = ObjectClassification + Object Localization
- 7. www.eliftech.com One model fortwo tasks? Object detection - output is the one number (index) of a class Object localization - output is the four numbers - coordinates of bounding box. Po bx1 bx2 by1 by2 c1 c2 c3 … cn - is object exists - bounding box coordinates - object’s variables
- 8. www.eliftech.com State of ObjectDetection
- 9. www.eliftech.com Approaches ▪ Classical approach(Haar features) - first OD real time framework (Viola-Jones) ▪ Deep learning approach - now state of the art in OD ▪ OverFeat ▪ R-CNN ▪ Fast R-CNN ▪ YOLO ▪ Faster R-CNN ▪ SSD and R-FCN
- 10. www.eliftech.com Deep learning approach OverFeat- published in 2013, multi-scale sliding window algorithm using Convolutional Neural Networks (CNNs). R-CNN - Regions with CNN features. Three stage approach: - Extract possible objects using a region proposal method (the most popular one being Selective Search). - Extract features from each region using a CNN. - Classify each region with SVMs.
- 11. www.eliftech.com Fast R-CNN -Similar to R-CNN, it used Selective Search to generate object proposals, but instead of extracting all of them independently and using SVM classifiers, it applied the CNN on the complete image and then used both Region of Interest (RoI) Pooling on the feature map with a final feed forward network for classification and regression. YOLO - You Only Look Once: a simple convolutional neural network approach which has both great results and high speed, allowing for the first time real time object detection. Deep learning approach
- 12. www.eliftech.com Faster R-CNN -Faster R-CNN added what they called a Region Proposal Network (RPN), in an attempt to get rid of the Selective Search algorithm and make the model completely trainable end-to-end. SSD and R-FCN Finally, there are two notable papers, Single Shot Detector (SSD) which takes on YOLO by using multiple sized convolutional feature maps achieving better results and speed, and Region-based Fully Convolutional Networks (R-FCN) which takes the architecture of Faster R-CNN but with only convolutional networks. Deep learning approach
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- 14. www.eliftech.com TF Object DetectionAPI ▪ Open Source from 2017-07-15 ▪ Built on top of TensorFlow ▪ Contains trainable detection models ▪ Contains frozen weights ▪ Contains Jupyter Notebook ▪ Makes easy to construct, train and deploy object detection models
- 15. www.eliftech.com Getting started ▪ Protobuf2.6 ▪ Python-tk ▪ Pillow 1.0 ▪ lxml ▪ Tf Slim (included) ▪ Jupyter notebook ▪ Matplotlib ▪ Tensorflow (tensorflow- gpu) ▪ Cython ▪ cocoapi Dependencies: If model will be trained locally - better to install tensorflow-gpu. Dependencies for tensorflow-gpu: ▪ NVIDIA GPU with CUDA Compute Capability 3.0 (list) ▪ Ubuntu 16.04 at least ▪ CUDA® Toolkit 9.0 ▪ NVIDIA drivers associated with CUDA Toolkit 9.0. ▪ cuDNN v7.0 ▪ libcupti-dev Installation instruction Installation instruction Latest version of CUDA Toolkit - 9.1 not compatible with tensorflow 1.6, need to install 9.0
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- 17. www.eliftech.com Dataset ▪ Tensorflow ObjectDetection API uses the TFRecord file format ▪ There is available third-party scripts to convert PASCAL VOC and Oxford Pet Format ▪ In other case explanation of format available in git repo. ▪ Input data to create TFRecord - annotated image
- 18. www.eliftech.com Getting images Grab frominternet ▪ Scrap images from google or Pixabay or whatever ▪ For batch downloading - Faktun Bulk Image Downloader ▪ For data mining by multiplying existing images - ImageMagic Create own images ▪ Record video with needed object/objects (in 640x480) ▪ Process video and split on screenshots - ffmpeg Tips ▪ Create images with different lights, background and so on. ▪ If object is able to have different forms - better to catch them all. ▪ Try to make 30%-50% of images with overlaid object ▪ Tool for image augmentation
- 19. www.eliftech.com Labeling (Annotation) animages Tools ▪ LabelImg ▪ FIAT (Fast Image Data Annotation Tool) ▪ input: images ▪ output: .xml files with bounding boxes coordinates
- 20. www.eliftech.com Creating TFRecord ▪ Tensorflowobject detection API repo contains folder dataset_tools with scripts to coverts common structures of data in TFRecord. ▪ If output data has another structure - here is explanation how to convert it
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- 22. www.eliftech.com Selecting a model TensorflowOD API provides a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, and the Open Images dataset. - model name corresponds to a config file that was used to train this model. - speed - running time in ms per 600x600 image - mAP stands for mean average precision, which indicates how well the model performed on the COCO dataset. - Outputs types (Boxes, and Masks if applicable)
- 23. www.eliftech.com Configuring ● label.pbtx ● pipeline.config train_config:{ fine_tune_checkpoint: "<path_to_model.ckpt>" num_steps: 200000 } train_input_reader { label_map_path: "<path_to_labels.pbtxt>" tf_record_input_reader { input_path: "<path_to_train.record>" } } eval_config { num_examples: 8000 max_evals: 10 use_moving_averages: false } eval_input_reader { label_map_path: "<path_to_labels.pbtxt>" shuffle: false num_readers: 1 tf_record_input_reader { input_path: "<path_to_test.record>" } } ● Folders structure instruction
- 24. www.eliftech.com Training & Evaluating #From the tensorflow/models/research directory python object_detection/eval.py --logtostderr --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} --checkpoint_dir=${PATH_TO_TRAIN_DIR} --eval_dir=${PATH_TO_EVAL_DIR} # From the tensorflow/models/research directory python object_detection/train.py --logtostderr -- pipeline_config_path=/tensorflow/models/object_detection/samples/configs/ssd_mobilenet_v1_p ets.config --train_dir=${PATH_TO_ROOT_TRAIN_FOLDER}
- 25. www.eliftech.com Links ▪ https://towardsdatascience.com/how-to-train-your-own-object-detector-with- tensorflows-object-detector-api-bec72ecfe1d9 ▪ https://www.kdnuggets.com/2017/10/deep-learning-object-detection- comprehensive-review.html ▪http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfreco rd.html ▪ https://www.coursera.org/learn/convolutional-neural-networks ▪ https://medium.com/comet-app/review-of-deep-learning-algorithms-for-object- detection-c1f3d437b852 ▪ https://towardsdatascience.com/evolution-of-object-detection-and-localization- algorithms-e241021d8bad ▪ https://medium.freecodecamp.org/how-to-play-quidditch-using-the-tensorflow- object-detection-api-b0742b99065d
- 26. www.eliftech.com Don’t forget tosubscribe! Find us at eliftech.com Have a question? Contact us: info@eliftech.com