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research/object_detection/README.md
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![TensorFlow Requirement: 1.15](https://img.shields.io/badge/TensorFlow%20Requirement-1.15-brightgreen)
![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
# Tensorflow Object Detection API
# TensorFlow Object Detection API
[![TensorFlow 2.2](https://img.shields.io/badge/TensorFlow-2.2-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0)
[![TensorFlow 1.15](https://img.shields.io/badge/TensorFlow-1.15-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v1.15.0)
[![Python 3.6](https://img.shields.io/badge/Python-3.6-3776AB)](https://www.python.org/downloads/release/python-360/)
Creating accurate machine learning models capable of localizing and identifying
multiple objects in a single image remains a core challenge in computer vision.
......@@ -11,7 +11,7 @@ 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
you if you find this API useful. Finally if you use the TensorFlow Object
Detection API for a research publication, please consider citing:
```
......@@ -26,91 +26,91 @@ Song Y, Guadarrama S, Murphy K, CVPR 2017
<img src="g3doc/img/tf-od-api-logo.png" width=140 height=195>
</p>
## Maintainers
## Support for TensorFlow 2 and 1
The TensorFlow Object Detection API supports both TensorFlow 2 (TF2) and
TensorFlow 1 (TF1). A majority of the modules in the library are both TF1 and
TF2 compatible. In cases where they are not, we provide two versions.
Name | GitHub
-------------- | ---------------------------------------------
Jonathan Huang | [jch1](https://github.com/jch1)
Vivek Rathod | [tombstone](https://github.com/tombstone)
Ronny Votel | [ronnyvotel](https://github.com/ronnyvotel)
Derek Chow | [derekjchow](https://github.com/derekjchow)
Chen Sun | [jesu9](https://github.com/jesu9)
Menglong Zhu | [dreamdragon](https://github.com/dreamdragon)
Alireza Fathi | [afathi3](https://github.com/afathi3)
Zhichao Lu | [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/tpu_exporters.md'>
Exporting a trained model for TPU 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 2018/2019</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>
* <a href='g3doc/context_rcnn.md'>
Context R-CNN documentation for data preparation, training, and export</a><br>
Although we will continue to maintain the TF1 models and provide support, we
encourage users to try the Object Detection API with TF2 for the following
reasons:
## Getting Help
* We provide new architectures supported in TF2 only and we will continue to
develop in TF2 going forward.
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".
* The popular models we ported from TF1 to TF2 achieve the same performance.
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".
* A single training and evaluation binary now supports both GPU and TPU
distribution strategies making it possible to train models with synchronous
SGD by default.
* Eager execution with new binaries makes debugging easy!
Finally, if are an existing user of the Object Detection API we have retained
the same config language you are familiar with and ensured that the
TF2 training/eval binary takes the same arguments as our TF1 binaries.
Note: The models we provide in [TF2 Zoo](g3doc/tf2_detection_zoo.md) and
[TF1 Zoo](g3doc/tf1_detection_zoo.md) are specific to the TensorFlow major
version and are not interoperable.
Please check [FAQ](g3doc/faq.md) for frequently asked questions before reporting
an issue.
Please select one of the two links below for TensorFlow version specific
documentation of the Object Detection API:
## Release information
### June 17th, 2020
<!-- mdlint off(WHITESPACE_LINE_LENGTH) -->
[![Object Detection API TensorFlow 2](https://img.shields.io/badge/Object%20Detection%20API-TensorFlow%202-orange)](g3doc/tf2.md) \
[![Object Detection API TensorFlow 1](https://img.shields.io/badge/Object%20Detection%20API-TensorFlow%201-orange)](g3doc/tf1.md)
<!-- mdlint on -->
## Whats New
### TensorFlow 2 Support
We are happy to announce that the TF OD API officially supports TF2! Our release
includes:
* New binaries for train/eval/export that are designed to run in eager mode.
* A suite of TF2 compatible (Keras-based) models; this includes migrations of
our most popular TF1.x models (e.g., SSD with MobileNet, RetinaNet,
Faster R-CNN, Mask R-CNN), as well as a few new architectures for which we
will only maintain TF2 implementations:
1. CenterNet - a simple and effective anchor-free architecture based on
the recent [Objects as Points](https://arxiv.org/abs/1904.07850) paper by
Zhou et al.
2. [EfficientDet](https://arxiv.org/abs/1911.09070) - a recent family of
SOTA models discovered with the help of Neural Architecture Search.
* COCO pre-trained weights for all of the models provided as TF2 style
object-based checkpoints.
* Access to [Distribution Strategies](https://www.tensorflow.org/guide/distributed_training)
for distributed training --- our model are designed to be trainable using sync
multi-GPU and TPU platforms.
* Colabs demo’ing eager mode training and inference.
See our release blogpost [here](https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html).
If you are an existing user of the TF OD API using TF 1.x, don’t worry, we’ve
got you covered.
**Thanks to contributors**: Akhil Chinnakotla, Allen Lavoie, Anirudh Vegesana,
Anjali Sridhar, Austin Myers, Dan Kondratyuk, David Ross, Derek Chow, Jaeyoun
Kim, Jing Li, Jonathan Huang, Jordi Pont-Tuset, Karmel Allison, Kathy Ruan,
Kaushik Shivakumar, Lu He, Mingxing Tan, Pengchong Jin, Ronny Votel, Sara Beery,
Sergi Caelles Prat, Shan Yang, Sudheendra Vijayanarasimhan, Tina Tian, Tomer
Kaftan, Vighnesh Birodkar, Vishnu Banna, Vivek Rathod, Yanhui Liang, Yiming Shi,
Yixin Shi, Yu-hui Chen, Zhichao Lu.
### Context R-CNN
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)),
* 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.
......@@ -118,282 +118,48 @@ Importantly, these contextual images need not be labeled.
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).
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).
[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).
the
[model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md#snapshot-serengeti-camera-trap-trained-models).
A colab demonstrating Context R-CNN is provided
[here](colab_tutorials/context_rcnn_tutorial.ipynb).
<b>Thanks to contributors</b>: Sara Beery, Jonathan Huang, Guanhang Wu, Vivek
Rathod, Ronny Votel, Zhichao Lu, David Ross, Pietro Perona, Tanya Birch, and
the Wildlife Insights AI Team.
### May 19th, 2020
We have released [MobileDets](https://arxiv.org/abs/2004.14525), a set of
high-performance models for mobile CPUs, DSPs and EdgeTPUs.
* MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile
CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by
1.9 mAP on mobile CPUs, 3.7 mAP on EdgeTPUs and 3.4 mAP on DSPs while
running equally fast. MobileDets also offer up to 2x speedup over MnasFPN on
EdgeTPUs and DSPs.
For each of the three hardware platforms we have released model definition,
model checkpoints trained on the COCO14 dataset and converted TFLite models in
fp32 and/or uint8.
<b>Thanks to contributors</b>: Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin
Akin, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen,
Quoc Le, Zhichao Lu.
### May 7th, 2020
We have released a mobile model with the
[MnasFPN head](https://arxiv.org/abs/1912.01106).
* MnasFPN with MobileNet-V2 backbone is the most accurate (26.6 mAP at 183ms
on Pixel 1) mobile detection model we have released to date. With
depth-multiplier, MnasFPN with MobileNet-V2 backbone is 1.8 mAP higher than
MobileNet-V3-Large with SSDLite (23.8 mAP vs 22.0 mAP) at similar latency
(120ms) on Pixel 1.
We have released model definition, model checkpoints trained on the COCO14
dataset and a converted TFLite model.
<b>Thanks to contributors</b>: Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi
Lin, Dmitry Kalenichenko, Hartwig Adam, Quoc Le, Zhichao Lu, Jonathan Huang, Hao
Xu.
### Nov 13th, 2019
We have released MobileNetEdgeTPU SSDLite model.
* SSDLite with MobileNetEdgeTPU backbone, which achieves 10% mAP higher than
MobileNetV2 SSDLite (24.3 mAP vs 22 mAP) on a Google Pixel4 at comparable
latency (6.6ms vs 6.8ms).
Along with the model definition, we are also releasing model checkpoints trained
on the COCO dataset.
<b>Thanks to contributors</b>: Yunyang Xiong, Bo Chen, Suyog Gupta, Hanxiao Liu,
Gabriel Bender, Mingxing Tan, Berkin Akin, Zhichao Lu, Quoc Le
### Oct 15th, 2019
We have released two MobileNet V3 SSDLite models (presented in
[Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)).
* SSDLite with MobileNet-V3-Large backbone, which is 27% faster than Mobilenet
V2 SSDLite (119ms vs 162ms) on a Google Pixel phone CPU at the same mAP.
* SSDLite with MobileNet-V3-Small backbone, which is 37% faster than MnasNet
SSDLite reduced with depth-multiplier (43ms vs 68ms) at the same mAP.
Along with the model definition, we are also releasing model checkpoints trained
on the COCO dataset.
<b>Thanks to contributors</b>: Bo Chen, Zhichao Lu, Vivek Rathod, Jonathan Huang
### July 1st, 2019
We have released an updated set of utils and an updated
[tutorial](g3doc/challenge_evaluation.md) for all three tracks of the
[Open Images Challenge 2019](https://storage.googleapis.com/openimages/web/challenge2019.html)!
The Instance Segmentation metric for
[Open Images V5](https://storage.googleapis.com/openimages/web/index.html) and
[Challenge 2019](https://storage.googleapis.com/openimages/web/challenge2019.html)
is part of this release. Check out
[the metric description](https://storage.googleapis.com/openimages/web/evaluation.html#instance_segmentation_eval)
on the Open Images website.
<b>Thanks to contributors</b>: Alina Kuznetsova, Rodrigo Benenson
### Feb 11, 2019
We have released detection models trained on the Open Images Dataset V4 in our
detection model zoo, including
Rathod, Ronny Votel, Zhichao Lu, David Ross, Pietro Perona, Tanya Birch, and the
Wildlife Insights AI Team.
* 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)
## Release Notes
See [notes](g3doc/release_notes.md) for all past releases.
<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).
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 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.
<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)).
See more information on the
[Open Images website](https://github.com/openimages/dataset)!
<b>Thanks to contributors</b>: Stefan Popov, Alina Kuznetsova
### November 6, 2017
We have re-released faster versions of our (pre-trained) models in the
<a href='g3doc/detection_model_zoo.md'>model zoo</a>. In addition to what was
available before, we are also adding Faster R-CNN models trained on COCO with
Inception V2 and Resnet-50 feature extractors, as well as a Faster R-CNN with
Resnet-101 model trained on the KITTI dataset.
<b>Thanks to contributors</b>: Jonathan Huang, Vivek Rathod, Derek Chow, Tal
Remez, Chen Sun.
### October 31, 2017
We have released a new state-of-the-art model for object detection using the
Faster-RCNN with the
[NASNet-A image featurization](https://arxiv.org/abs/1707.07012). This model
achieves mAP of 43.1% on the test-dev validation dataset for COCO, improving on
the best available model in the zoo by 6% in terms of absolute mAP.
<b>Thanks to contributors</b>: Barret Zoph, Vijay Vasudevan, Jonathon Shlens,
Quoc Le
### August 11, 2017
## Getting Help
We have released an update to the
[Android Detect demo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android)
which will now run models trained using the Tensorflow Object Detection API on
an Android device. By default, it currently runs a frozen SSD w/Mobilenet
detector trained on COCO, but we encourage you to try out other detection
models!
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".
<b>Thanks to contributors</b>: Jonathan Huang, Andrew Harp
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".
### June 15, 2017
Please check the [FAQ](g3doc/faq.md) for frequently asked questions before
reporting an issue.
In addition to our base Tensorflow detection model definitions, this release
includes:
## Maintainers
* 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](colab_tutorials/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
* Jonathan Huang ([@GitHub jch1](https://github.com/jch1))
* Vivek Rathod ([@GitHub tombstone](https://github.com/tombstone))
* Vighnesh Birodkar ([@GitHub vighneshbirodkar](https://github.com/vighneshbirodkar))
* Austin Myers ([@GitHub austin-myers](https://github.com/austin-myers))
* Zhichao Lu ([@GitHub pkulzc](https://github.com/pkulzc))
* Ronny Votel ([@GitHub ronnyvotel](https://github.com/ronnyvotel))
* Yu-hui Chen ([@GitHub yuhuichen1015](https://github.com/yuhuichen1015))
* Derek Chow ([@GitHub derekjchow](https://github.com/derekjchow))
research/object_detection/configs/tf2/center_net_hourglass104_1024x1024_coco17_tpu-32.config 0 → 100644
View file @ 5245161c
# CenterNet meta-architecture from the "Objects as Points" [2] paper with the
# hourglass[1] backbone.
# [1]: https://arxiv.org/abs/1603.06937
# [2]: https://arxiv.org/abs/1904.07850
# Trained on COCO, initialized from Extremenet Detection checkpoint
# Train on TPU-32 v3
#
# Achieves 44.6 mAP on COCO17 Val
model {
center_net {
num_classes: 90
feature_extractor {
type: "hourglass_104"
bgr_ordering: true
channel_means: [104.01362025, 114.03422265, 119.9165958 ]
channel_stds: [73.6027665 , 69.89082075, 70.9150767 ]
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 1024
max_dimension: 1024
pad_to_max_dimension: true
}
}
object_detection_task {
task_loss_weight: 1.0
offset_loss_weight: 1.0
scale_loss_weight: 0.1
localization_loss {
l1_localization_loss {
}
}
}
object_center_params {
object_center_loss_weight: 1.0
min_box_overlap_iou: 0.7
max_box_predictions: 100
classification_loss {
penalty_reduced_logistic_focal_loss {
alpha: 2.0
beta: 4.0
}
}
}
}
}
train_config: {
batch_size: 128
num_steps: 50000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_adjust_brightness {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
optimizer {
adam_optimizer: {
epsilon: 1e-7 # Match tf.keras.optimizers.Adam's default.
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 1e-3
total_steps: 50000
warmup_learning_rate: 2.5e-4
warmup_steps: 5000
}
}
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-1"
fine_tune_checkpoint_type: "detection"
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/center_net_hourglass104_512x512_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# CenterNet meta-architecture from the "Objects as Points" [2] paper with the
# hourglass[1] backbone.
# [1]: https://arxiv.org/abs/1603.06937
# [2]: https://arxiv.org/abs/1904.07850
# Trained on COCO, initialized from Extremenet Detection checkpoint
# Train on TPU-8
#
# Achieves 41.9 mAP on COCO17 Val
model {
center_net {
num_classes: 90
feature_extractor {
type: "hourglass_104"
bgr_ordering: true
channel_means: [104.01362025, 114.03422265, 119.9165958 ]
channel_stds: [73.6027665 , 69.89082075, 70.9150767 ]
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 512
max_dimension: 512
pad_to_max_dimension: true
}
}
object_detection_task {
task_loss_weight: 1.0
offset_loss_weight: 1.0
scale_loss_weight: 0.1
localization_loss {
l1_localization_loss {
}
}
}
object_center_params {
object_center_loss_weight: 1.0
min_box_overlap_iou: 0.7
max_box_predictions: 100
classification_loss {
penalty_reduced_logistic_focal_loss {
alpha: 2.0
beta: 4.0
}
}
}
}
}
train_config: {
batch_size: 128
num_steps: 140000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_crop_image {
min_aspect_ratio: 0.5
max_aspect_ratio: 1.7
random_coef: 0.25
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_adjust_brightness {
}
}
data_augmentation_options {
random_absolute_pad_image {
max_height_padding: 200
max_width_padding: 200
pad_color: [0, 0, 0]
}
}
optimizer {
adam_optimizer: {
epsilon: 1e-7 # Match tf.keras.optimizers.Adam's default.
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 1e-3
schedule {
step: 90000
learning_rate: 1e-4
}
schedule {
step: 120000
learning_rate: 1e-5
}
}
}
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-1"
fine_tune_checkpoint_type: "detection"
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/center_net_resnet101_v1_fpn_512x512_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# CenterNet meta-architecture from the "Objects as Points" [1] paper
# with the ResNet-v1-101 FPN backbone.
# [1]: https://arxiv.org/abs/1904.07850
# Train on TPU-8
#
# Achieves 34.18 mAP on COCO17 Val
model {
center_net {
num_classes: 90
feature_extractor {
type: "resnet_v2_101"
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 512
max_dimension: 512
pad_to_max_dimension: true
}
}
object_detection_task {
task_loss_weight: 1.0
offset_loss_weight: 1.0
scale_loss_weight: 0.1
localization_loss {
l1_localization_loss {
}
}
}
object_center_params {
object_center_loss_weight: 1.0
min_box_overlap_iou: 0.7
max_box_predictions: 100
classification_loss {
penalty_reduced_logistic_focal_loss {
alpha: 2.0
beta: 4.0
}
}
}
}
}
train_config: {
batch_size: 128
num_steps: 140000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_crop_image {
min_aspect_ratio: 0.5
max_aspect_ratio: 1.7
random_coef: 0.25
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_adjust_brightness {
}
}
data_augmentation_options {
random_absolute_pad_image {
max_height_padding: 200
max_width_padding: 200
pad_color: [0, 0, 0]
}
}
optimizer {
adam_optimizer: {
epsilon: 1e-7 # Match tf.keras.optimizers.Adam's default.
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 1e-3
schedule {
step: 90000
learning_rate: 1e-4
}
schedule {
step: 120000
learning_rate: 1e-5
}
}
}
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/weights-1"
fine_tune_checkpoint_type: "classification"
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet101_v1_1024x1024_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-101 (v1),
# w/high res inputs, long training schedule
# Trained on COCO, initialized from Imagenet classification checkpoint
#
# Train on TPU-8
#
# Achieves 37.1 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
fixed_shape_resizer {
width: 1024
height: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet101_keras'
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 64
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 100000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 100000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet101.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet101_v1_640x640_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-50 (v1)
# Trained on COCO, initialized from Imagenet classification checkpoint
#
# Train on TPU-8
#
# Achieves 31.8 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 640
max_dimension: 640
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet101_keras'
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 64
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 25000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 25000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet101.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet101_v1_800x1333_coco17_gpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-101 (v1),
# Initialized from Imagenet classification checkpoint
#
# Train on GPU-8
#
# Achieves 36.6 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1333
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet101_keras'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 16
num_steps: 200000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.01
total_steps: 200000
warmup_learning_rate: 0.0
warmup_steps: 5000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet101.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet152_v1_1024x1024_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-152 (v1)
# w/high res inputs, long training schedule
# Trained on COCO, initialized from Imagenet classification checkpoint
#
# Train on TPU-8
#
# Achieves 37.6 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
fixed_shape_resizer {
width: 1024
height: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet152_keras'
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 64
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 100000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 100000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet152.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet152_v1_640x640_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-152 (v1)
# Trained on COCO, initialized from Imagenet classification checkpoint
#
# Train on TPU-8
#
# Achieves 32.4 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 640
max_dimension: 640
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet152_keras'
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 64
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 25000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 25000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet152.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-152 (v1),
# Initialized from Imagenet classification checkpoint
#
# Train on GPU-8
#
# Achieves 37.3 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1333
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet152_keras'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 16
num_steps: 200000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.01
total_steps: 200000
warmup_learning_rate: 0.0
warmup_steps: 5000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet152.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet50_v1_1024x1024_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-50 (v1),
# w/high res inputs, long training schedule
# Trained on COCO, initialized from Imagenet classification checkpoint
#
# Train on TPU-8
#
# Achieves 31.0 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
fixed_shape_resizer {
width: 1024
height: 1024
}
}
feature_extractor {
type: 'faster_rcnn_resnet50_keras'
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 64
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 100000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 100000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet50.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet50_v1_640x640_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-50 (v1) with 640x640 input resolution
# Trained on COCO, initialized from Imagenet classification checkpoint
#
# Train on TPU-8
#
# Achieves 29.3 mAP on COCO17 Val
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 640
max_dimension: 640
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet50_keras'
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_static_shapes: true
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_static_balanced_label_sampler: true
use_matmul_gather_in_matcher: true
}
}
train_config: {
batch_size: 64
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 25000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .04
total_steps: 25000
warmup_learning_rate: .013333
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet50.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true # works only on TPUs
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/faster_rcnn_resnet50_v1_800x1333_coco17_gpu-8.config 0 → 100644
View file @ 5245161c
# Faster R-CNN with Resnet-50 (v1),
# Initialized from Imagenet classification checkpoint
#
# Train on GPU-8
#
# Achieves 31.4 mAP on COCO17 val
model {
faster_rcnn {
num_classes: 90
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1333
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_resnet50_keras'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 16
num_steps: 200000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.01
total_steps: 200000
warmup_learning_rate: 0.0
warmup_steps: 5000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/resnet50.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config 0 → 100644
View file @ 5245161c
# Mask R-CNN with Inception Resnet v2 (no atrous)
# Sync-trained on COCO (with 8 GPUs) with batch size 16 (1024x1024 resolution)
# Initialized from Imagenet classification checkpoint
#
# Train on GPU-8
#
# Achieves 40.4 box mAP and 35.5 mask mAP on COCO17 val
model {
faster_rcnn {
number_of_stages: 3
num_classes: 90
image_resizer {
fixed_shape_resizer {
height: 1024
width: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_resnet_v2_keras'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 17
maxpool_kernel_size: 1
maxpool_stride: 1
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
mask_height: 33
mask_width: 33
mask_prediction_conv_depth: 0
mask_prediction_num_conv_layers: 4
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
predict_instance_masks: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
second_stage_mask_prediction_loss_weight: 4.0
resize_masks: false
}
}
train_config: {
batch_size: 16
num_steps: 200000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.008
total_steps: 200000
warmup_learning_rate: 0.0
warmup_steps: 5000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/inception_resnet_v2.ckpt-1"
fine_tune_checkpoint_type: "classification"
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
load_instance_masks: true
mask_type: PNG_MASKS
}
eval_config: {
metrics_set: "coco_detection_metrics"
metrics_set: "coco_mask_metrics"
eval_instance_masks: true
use_moving_averages: false
batch_size: 1
include_metrics_per_category: true
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
load_instance_masks: true
mask_type: PNG_MASKS
}
research/object_detection/configs/tf2/ssd_efficientdet_d0_512x512_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# SSD with EfficientNet-b0 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d0).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b0 checkpoint.
#
# Train on TPU-8
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 90
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 512
max_dimension: 512
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 64
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 3
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b0_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 3
num_filters: 64
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "classification"
batch_size: 128
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 512
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 8e-2
total_steps: 300000
warmup_learning_rate: .001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/ssd_efficientdet_d1_640x640_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# SSD with EfficientNet-b1 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d1).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b1 checkpoint.
#
# Train on TPU-8
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 90
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 640
max_dimension: 640
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 88
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 3
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b1_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 4
num_filters: 88
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "classification"
batch_size: 128
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 640
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 8e-2
total_steps: 300000
warmup_learning_rate: .001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/ssd_efficientdet_d2_768x768_coco17_tpu-8.config 0 → 100644
View file @ 5245161c
# SSD with EfficientNet-b2 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d2).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b2 checkpoint.
#
# Train on TPU-8
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 90
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 768
max_dimension: 768
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 112
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 3
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b2_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 5
num_filters: 112
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "classification"
batch_size: 128
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 768
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 8e-2
total_steps: 300000
warmup_learning_rate: .001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/ssd_efficientdet_d3_896x896_coco17_tpu-32.config 0 → 100644
View file @ 5245161c
# SSD with EfficientNet-b3 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d3).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b3 checkpoint.
#
# Train on TPU-32
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 90
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 896
max_dimension: 896
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 160
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 4
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b3_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 6
num_filters: 160
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "classification"
batch_size: 128
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 896
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 8e-2
total_steps: 300000
warmup_learning_rate: .001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/ssd_efficientdet_d4_1024x1024_coco17_tpu-32.config 0 → 100644
View file @ 5245161c
# SSD with EfficientNet-b4 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d4).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b4 checkpoint.
#
# Train on TPU-32
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 90
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 1024
max_dimension: 1024
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 224
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 4
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b4_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 7
num_filters: 224
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "classification"
batch_size: 128
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 1024
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 8e-2
total_steps: 300000
warmup_learning_rate: .001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
research/object_detection/configs/tf2/ssd_efficientdet_d5_1280x1280_coco17_tpu-32.config 0 → 100644
View file @ 5245161c
# SSD with EfficientNet-b5 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d5).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b5 checkpoint.
#
# Train on TPU-32
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 90
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 1280
max_dimension: 1280
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 288
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 4
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b5_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 7
num_filters: 288
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "classification"
batch_size: 128
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 1280
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 8e-2
total_steps: 300000
warmup_learning_rate: .001
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
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