# 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
### 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 contributors</b>: Menglong Zhu, Mark Sandler, Zhichao Lu, Vivek Rathod, Jonathan Huang
### February 9, 2018
We now support instance segmentation!! In this API update we support a number of instance segmentation models similar to those discussed in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). For further details refer to
[our slides](http://presentations.cocodataset.org/Places17-GMRI.pdf) from the 2017 Coco + Places Workshop.
Refer to the section on [Running an Instance Segmentation Model](g3doc/instance_se
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Python车辆检测和型号分类识别系统源码+数据集,对任意车辆图片进行车辆检测和型号分类的识别 (576个子文件)
ssdlite_mobilenet_v2_coco.config 5KB
ssdlite_mobilenet_v1_coco.config 5KB
ssd_mobilenet_v2_coco.config 5KB
ssd_mobilenet_v1_coco.config 5KB
ssd_mobilenet_v1_pets.config 5KB
ssd_inception_v2_coco.config 5KB
ssd_inception_v2_pets.config 5KB
ssd_mobilenet_v1_0.75_depth_quantized_300x300_pets_sync.config 4KB
ssd_inception_v3_pets.config 4KB
ssd_mobilenet_v1_focal_loss_pets.config 4KB
ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync.config 4KB
ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config 4KB
ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync.config 4KB
ssd_mobilenet_v1_quantized_300x300_coco14_sync.config 4KB
ssd_mobilenet_v1_300x300_coco14_sync.config 4KB
ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync.config 4KB
mask_rcnn_inception_resnet_v2_atrous_coco.config 4KB
mask_rcnn_resnet101_atrous_coco.config 4KB
mask_rcnn_resnet50_atrous_coco.config 4KB
mask_rcnn_inception_v2_coco.config 4KB
ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync.config 4KB
embedded_ssd_mobilenet_v1_coco.config 4KB
mask_rcnn_resnet101_pets.config 4KB
faster_rcnn_nas_coco.config 4KB
faster_rcnn_inception_resnet_v2_atrous_oid.config 4KB
faster_rcnn_inception_resnet_v2_atrous_coco.config 4KB
faster_rcnn_inception_resnet_v2_atrous_pets.config 4KB
faster_rcnn_inception_v2_coco.config 4KB
faster_rcnn_resnet152_coco.config 4KB
faster_rcnn_resnet50_coco.config 4KB
faster_rcnn_resnet50_pets.config 4KB
faster_rcnn_resnet101_pets.config 4KB
faster_rcnn_inception_v2_pets.config 4KB
faster_rcnn_resnet152_pets.config 4KB
rfcn_resnet101_coco.config 4KB
rfcn_resnet101_pets.config 4KB
faster_rcnn_resnet101_kitti.config 3KB
faster_rcnn_resnet101_atrous_coco.config 3KB
faster_rcnn_inception_resnet_v2_atrous_cosine_lr_coco.config 3KB
faster_rcnn_resnet101_coco.config 3KB
faster_rcnn_resnet101_ava_v2.1.config 3KB
faster_rcnn_resnet101_voc07.config 3KB
Dockerfile 6KB
object_detection_tutorial.ipynb 15KB
vehicle_detection.ipynb 15KB
image2.jpg 1.35MB
kites_detections_output.jpg 377KB
dogs_detections_output.jpg 364KB
oid_monkey_3b4168c89cecbc5b.jpg 275KB
oid_bus_72e19c28aac34ed8.jpg 243KB
example_cat.jpg 238KB
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test.jpg 70KB
image_detection.jpg 21KB
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result1.jpg 6KB
crop1.jpg 6KB
my_inception_v4_freeze.label 14KB
running_pets.md 13KB
README.md 11KB
oid_inference_and_evaluation.md 11KB
detection_model_zoo.md 10KB
running_on_mobile_tensorflowlite.md 8KB
challenge_evaluation.md 8KB
using_your_own_dataset.md 7KB
defining_your_own_model.md 7KB
evaluation_protocols.md 7KB
running_on_cloud.md 7KB
tpu_compatibility.md 7KB
configuring_jobs.md 6KB
instance_segmentation.md 5KB
installation.md 4KB
README.md 3KB
preparing_inputs.md 2KB
running_locally.md 2KB
exporting_models.md 1KB
faq.md 1KB
CONTRIBUTING.md 765B
README.md 665B
running_notebook.md 561B
ssd_inception_v2.pb 97.74MB
frozen_inference_graph2.pb 23.04MB
frozen_inference_graph1.pb 21.6MB
oid_bbox_trainable_label_map.pbtxt 35KB
oid_object_detection_challenge_500_label_map.pbtxt 32KB
mscoco_label_map.pbtxt 5KB
ava_label_map_v2.1.pbtxt 3KB
pet_label_map.pbtxt 1KB
pascal_label_map.pbtxt 705B
kitti_label_map.pbtxt 70B
项目总结报告.pdf 1.65MB
第四阶段总结.pdf 556KB
第三阶段总结.pdf 448KB
第一阶段总结.pdf 220KB
第二阶段总结.pdf 149KB
kites_with_segment_overlay.png 6.83MB
dataset_explorer.png 4.22MB
test.png 959KB
oxford_pet.png 270KB
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