Implementation of the Neural Programmer model described in [paper](https://openreview.net/pdf?id=ry2YOrcge)
Implementation of the Neural Programmer model as described in this [paper](https://openreview.net/pdf?id=ry2YOrcge).
Download and extract the data from [dropbox](https://www.dropbox.com/s/9tvtcv6lmy51zfw/data.zip?dl=0). Change the ``data_dir FLAG`` to the location of the data.
Download and extract the data from the [WikiTableQuestions](https://ppasupat.github.io/WikiTableQuestions/) site. The dataset contains
11321, 2831, and 4344 examples for training, development, and testing respectively. We use their tokenization, number and date pre-processing. Please note that the above paper used the [initial release](https://github.com/ppasupat/WikiTableQuestions/releases/tag/v0.2) for training, development and testing.
Change the `data_dir FLAG` to the location of the data.
Running object detection on mobile devices with TensorFlow Lite</a><br>
*<ahref='g3doc/context_rcnn.md'>
Context R-CNN documentation for data preparation, training, and export</a><br>
## Getting Help
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@@ -98,78 +99,107 @@ 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.
Please check [FAQ](g3doc/faq.md) for frequently asked questions before reporting
an issue.
## Release information
### June 17th, 2020
We have released [Context R-CNN](https://arxiv.org/abs/1912.03538), a model that
uses attention to incorporate contextual information images (e.g. from
temporally nearby frames taken by a static camera) in order to improve accuracy.
Importantly, these contextual images need not be labeled.
* When applied to a challenging wildlife detection dataset ([Snapshot Serengeti](http://lila.science/datasets/snapshot-serengeti)),
Context R-CNN with context from up to a month of images outperforms a
single-frame baseline by 17.9% mAP, and outperforms S3D (a 3d convolution
based baseline) by 11.2% mAP.
* Context R-CNN leverages temporal context from the unlabeled frames of a
novel camera deployment to improve performance at that camera, boosting
model generalizeability.
Read about Context R-CNN on the Google AI blog [here](https://ai.googleblog.com/2020/06/leveraging-temporal-context-for-object.html).
We have provided code for generating data with associated context
[here](g3doc/context_rcnn.md), and a sample config for a Context R-CNN
model [here](samples/configs/context_rcnn_resnet101_snapshot_serengeti_sync.config).
Snapshot Serengeti-trained Faster R-CNN and Context R-CNN models can be found in
the [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#snapshot-serengeti-camera-trap-trained-models).
*Faster R-CNN detector with Inception Resnet V2 feature extractor
*SSD detector with MobileNet V2 feature extractor
*SSD detector with ResNet 101 FPN feature extractor (aka RetinaNet-101)
<b>Thanks to contributors</b>: Alina Kuznetsova, Yinxiao Li
### 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).
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).
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@@ -210,42 +242,59 @@ For more details please refer to this [paper](https://arxiv.org/abs/1707.06642).
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
* 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).
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).
We have released the implementation of evaluation metrics for both tracks of the
<b>Thanks to contributors</b>: Alina Kuznetsova, Vittorio Ferrari, Jasper Uijlings
<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.
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
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@@ -255,84 +304,94 @@ For more details please refer to this [paper](https://arxiv.org/abs/1705.08421).
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.
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
<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_segmentation.md) for instructions on how to configure a model
that predicts masks in addition to object bounding boxes.
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_segmentation.md) for
instructions on how to configure a model that predicts masks in addition to
object bounding boxes.
<b>Thanks to contributors</b>: Alireza Fathi, Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang
<b>Thanks to contributors</b>: Alireza Fathi, Zhichao Lu, Vivek Rathod, Ronny
Votel, Jonathan Huang
### November 17, 2017
As a part of the Open Images V3 release we have released:
* An implementation of the Open Images evaluation metric and the [protocol](g3doc/evaluation_protocols.md#open-images).
* Additional tools to separate inference of detection and evaluation (see [this tutorial](g3doc/oid_inference_and_evaluation.md)).
* A new detection model trained on the Open Images V2 data release (see [Open Images model](g3doc/detection_model_zoo.md#open-images-models)).
* An implementation of the Open Images evaluation metric and the
