# 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
```
\[[link](https://arxiv.org/abs/1611.10012)\]\[[bibtex](
https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAWUIIlnPZ_L9jxvPwcC49kDlELtaeIyU-&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1)\]
## Maintainers
* Jonathan Huang, github: [jch1](https://github.com/jch1)
* Vivek Rathod, github: [tombstone](https://github.com/tombstone)
* Derek Chow, github: [derekjchow](https://github.com/derekjchow)
* Chen Sun, github: [jesu9](https://github.com/jesu9)
* Menglong Zhu, github: [dreamdragon](https://github.com/dreamdragon)
## Table of contents
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>
Setup:
* <a href='g3doc/installation.md'>Installation</a><br>
* <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>
## Release information
### June 15, 2017
In addition to our base Tensorflow detection model definitions, this
release includes:
* A selection of trainable detection models, including:
* Single Shot Multibox Detector (SSD) with MobileNet,
* SSD with Inception V2,
* Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101,
* Faster RCNN with Resnet 101,
* Faster RCNN with Inception Resnet v2
* Frozen weights (trained on the COCO dataset) for each of the above models to
be used for out-of-the-box inference purposes.
* A [Jupyter notebook](object_detection_tutorial.ipynb) for performing
out-of-the-box inference with one of our released models
* Convenient [local training](g3doc/running_locally.md) scripts as well as
distributed training and evaluation pipelines via
[Google Cloud](g3doc/running_on_cloud.md).
<b>Thanks to contributors</b>: Jonathan Huang, Vivek Rathod, Derek Chow,
Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings,
Viacheslav Kovalevskyi, Kevin Murphy
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tensorflow基于cpu的检测
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利用python进行tensorflow的检测,亲测有效。基于cpu的操作,接下来就是gpu的
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tensorflow基于cpu的检测 (258个子文件)
BUILD 9KB
BUILD 6KB
BUILD 6KB
BUILD 5KB
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BUILD 1KB
BUILD 942B
BUILD 649B
ssd_mobilenet_v1_pets.config 4KB
ssd_inception_v2_pets.config 4KB
faster_rcnn_inception_resnet_v2_atrous_pets.config 3KB
faster_rcnn_resnet101_voc07.config 3KB
faster_rcnn_resnet101_pets.config 3KB
faster_rcnn_resnet152_pets.config 3KB
faster_rcnn_resnet50_pets.config 3KB
rfcn_resnet101_pets.config 3KB
.gitignore 1KB
object_detection_tutorial.ipynb 12KB
image2.jpg 1.35MB
kites_detections_output.jpg 377KB
dogs_detections_output.jpg 364KB
image1.jpg 127KB
LICENSE 1KB
running_pets.md 12KB
defining_your_own_model.md 7KB
configuring_jobs.md 5KB
running_on_cloud.md 5KB
README.md 3KB
running_locally.md 3KB
detection_model_zoo.md 2KB
installation.md 2KB
preparing_inputs.md 2KB
README.md 1KB
exporting_models.md 792B
CONTRIBUTING.md 765B
running_notebook.md 543B
frozen_inference_graph.pb 27.83MB
mscoco_label_map.pbtxt 5KB
pet_label_map.pbtxt 2KB
pascal_label_map.pbtxt 751B
oxford_pet.png 270KB
tensorboard2.png 231KB
tensorboard.png 77KB
preprocessor.proto 11KB
faster_rcnn.proto 5KB
losses.proto 4KB
box_predictor.proto 3KB
hyperparams.proto 3KB
optimizer.proto 2KB
train.proto 2KB
input_reader.proto 2KB
ssd.proto 2KB
eval.proto 2KB
post_processing.proto 1KB
grid_anchor_generator.proto 1020B
argmax_matcher.proto 979B
ssd_anchor_generator.proto 952B
image_resizer.proto 927B
string_int_label_map.proto 724B
region_similarity_calculator.proto 672B
pipeline.proto 633B
box_coder.proto 552B
faster_rcnn_box_coder.proto 531B
anchor_generator.proto 477B
square_box_coder.proto 419B
matcher.proto 418B
model.proto 295B
mean_stddev_box_coder.proto 188B
bipartite_matcher.proto 179B
preprocessor_pb2.py 76KB
preprocessor.py 75KB
preprocessor_test.py 74KB
faster_rcnn_meta_arch.py 68KB
faster_rcnn_meta_arch_test_lib.py 42KB
box_list_ops_test.py 41KB
ops_test.py 41KB
box_list_ops.py 36KB
target_assigner_test.py 29KB
post_processing_test.py 29KB
ops.py 27KB
ssd_meta_arch.py 26KB
losses_test.py 25KB
eval_util.py 24KB
box_predictor.py 24KB
losses_pb2.py 24KB
losses.py 23KB
optimizer_pb2.py 22KB
hyperparams_pb2.py 21KB
np_box_list_ops.py 20KB
target_assigner.py 20KB
box_predictor_pb2.py 18KB
visualization_utils.py 17KB
faster_rcnn_pb2.py 17KB
np_box_list_ops_test.py 16KB
preprocessor_builder_test.py 16KB
hyperparams_builder_test.py 15KB
box_predictor_builder_test.py 15KB
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