python毕业设计基于视频的行人流量密度检测项目源码+全部资料（高分项目）.zip

# Tensorflow Object Detection API Creating accurate machine learning models capable of localizing and identifying multiple objects in a single image remains a core challenge in computer vision. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. At Google we’ve certainly found this codebase to be useful for our computer vision needs, and we hope that you will as well. <p align="center"> <img src="g3doc/img/kites_detections_output.jpg" width=676 height=450> </p> Contributions to the codebase are welcome and we would love to hear back from you if you find this API useful. Finally if you use the Tensorflow Object Detection API for a research publication, please consider citing: ``` "Speed/accuracy trade-offs for modern convolutional object detectors." Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, Murphy K, CVPR 2017 ``` \[[link](https://arxiv.org/abs/1611.10012)\]\[[bibtex]( https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAWUIIlnPZ_L9jxvPwcC49kDlELtaeIyU-&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1)\] <p align="center"> <img src="g3doc/img/tf-od-api-logo.png" width=140 height=195> </p> ## Maintainers * Jonathan Huang, github: [jch1](https://github.com/jch1) * Vivek Rathod, github: [tombstone](https://github.com/tombstone) * Ronny Votel, github: [ronnyvotel](https://github.com/ronnyvotel) * Derek Chow, github: [derekjchow](https://github.com/derekjchow) * Chen Sun, github: [jesu9](https://github.com/jesu9) * Menglong Zhu, github: [dreamdragon](https://github.com/dreamdragon) * Alireza Fathi, github: [afathi3](https://github.com/afathi3) * Zhichao Lu, github: [pkulzc](https://github.com/pkulzc) ## Table of contents Setup: * <a href='g3doc/installation.md'>Installation</a><br> Quick Start: * <a href='object_detection_tutorial.ipynb'> Quick Start: Jupyter notebook for off-the-shelf inference</a><br> * <a href="g3doc/running_pets.md">Quick Start: Training a pet detector</a><br> Customizing a Pipeline: * <a href='g3doc/configuring_jobs.md'> Configuring an object detection pipeline</a><br> * <a href='g3doc/preparing_inputs.md'>Preparing inputs</a><br> Running: * <a href='g3doc/running_locally.md'>Running locally</a><br> * <a href='g3doc/running_on_cloud.md'>Running on the cloud</a><br> Extras: * <a href='g3doc/detection_model_zoo.md'>Tensorflow detection model zoo</a><br> * <a href='g3doc/exporting_models.md'> Exporting a trained model for inference</a><br> * <a href='g3doc/defining_your_own_model.md'> Defining your own model architecture</a><br> * <a href='g3doc/using_your_own_dataset.md'> Bringing in your own dataset</a><br> * <a href='g3doc/evaluation_protocols.md'> Supported object detection evaluation protocols</a><br> * <a href='g3doc/oid_inference_and_evaluation.md'> Inference and evaluation on the Open Images dataset</a><br> * <a href='g3doc/instance_segmentation.md'> Run an instance segmentation model</a><br> * <a href='g3doc/challenge_evaluation.md'> Run the evaluation for the Open Images Challenge 2018</a><br> * <a href='g3doc/tpu_compatibility.md'> TPU compatible detection pipelines</a><br> * <a href='g3doc/running_on_mobile_tensorflowlite.md'> Running object detection on mobile devices with TensorFlow Lite</a><br> ## Getting Help To get help with issues you may encounter using the Tensorflow Object Detection API, create a new question on [StackOverflow](https://stackoverflow.com/) with the tags "tensorflow" and "object-detection". Please report bugs (actually broken code, not usage questions) to the tensorflow/models GitHub [issue tracker](https://github.com/tensorflow/models/issues), prefixing the issue name with "object_detection". Please check [FAQ](g3doc/faq.md) for frequently asked questions before reporting an issue. ## Release information ### Sep 17, 2018 We have released Faster R-CNN detectors with ResNet-50 / ResNet-101 feature extractors trained on the [iNaturalist Species Detection Dataset](https://github.com/visipedia/inat_comp/blob/master/2017/README.md#bounding-boxes). The models are trained on the training split of the iNaturalist data for 4M iterations, they achieve 55% and 58% mean AP@.5 over 2854 classes respectively. For more details please refer to this [paper](https://arxiv.org/abs/1707.06642). <b>Thanks to contributors</b>: Chen Sun ### July 13, 2018 There are many new updates in this release, extending the functionality and capability of the API: * Moving from slim-based training to [Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)-based training. * Support for [RetinaNet](https://arxiv.org/abs/1708.02002), and a [MobileNet](https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html) adaptation of RetinaNet. * A novel SSD-based architecture called the [Pooling Pyramid Network](https://arxiv.org/abs/1807.03284) (PPN). * Releasing several [TPU](https://cloud.google.com/tpu/)-compatible models. These can be found in the `samples/configs/` directory with a comment in the pipeline configuration files indicating TPU compatibility. * Support for quantized training. * Updated documentation for new binaries, Cloud training, and [Tensorflow Lite](https://www.tensorflow.org/mobile/tflite/). See also our [expanded announcement blogpost](https://ai.googleblog.com/2018/07/accelerated-training-and-inference-with.html) and accompanying tutorial at the [TensorFlow blog](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193). <b>Thanks to contributors</b>: Sara Robinson, Aakanksha Chowdhery, Derek Chow, Pengchong Jin, Jonathan Huang, Vivek Rathod, Zhichao Lu, Ronny Votel ### June 25, 2018 Additional evaluation tools for the [Open Images Challenge 2018](https://storage.googleapis.com/openimages/web/challenge.html) are out. Check out our short tutorial on data preparation and running evaluation [here](g3doc/challenge_evaluation.md)! <b>Thanks to contributors</b>: Alina Kuznetsova ### June 5, 2018 We have released the implementation of evaluation metrics for both tracks of the [Open Images Challenge 2018](https://storage.googleapis.com/openimages/web/challenge.html) as a part of the Object Detection API - see the [evaluation protocols](g3doc/evaluation_protocols.md) for more details. Additionally, we have released a tool for hierarchical labels expansion for the Open Images Challenge: check out [oid_hierarchical_labels_expansion.py](dataset_tools/oid_hierarchical_labels_expansion.py). <b>Thanks to contributors</b>: Alina Kuznetsova, Vittorio Ferrari, Jasper Uijlings ### April 30, 2018 We have released a Faster R-CNN detector with ResNet-101 feature extractor trained on [AVA](https://research.google.com/ava/) v2.1. Compared with other commonly used object detectors, it changes the action classification loss function to per-class Sigmoid loss to handle boxes with multiple labels. The model is trained on the training split of AVA v2.1 for 1.5M iterations, it achieves mean AP of 11.25% over 60 classes on the validation split of AVA v2.1. For more details please refer to this [paper](https://arxiv.org/abs/1705.08421). <b>Thanks to contributors</b>: Chen Sun, David Ross ### April 2, 2018 Supercharge your mobile phones with the next generation mobile object detector! We are adding support for MobileNet V2 with SSDLite presented in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381). This model is 35% faster than Mobilenet V1 SSD on a Google Pixel phone CPU (200ms vs. 270ms) at the same accuracy. Along with the model definition, we are also releasing a model checkpoint trained on the COCO dataset. <b>Thanks to contr