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Fixing typos #10923

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cf1b9a0
Fixing typos
sushreebarsa Feb 3, 2023
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Fixing typos
sushreebarsa Feb 3, 2023
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14 changes: 7 additions & 7 deletions research/object_detection/README.md
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Expand Up @@ -7,10 +7,10 @@

*Note to our users*: the Tensorflow Object Detection API is no longer being
maintained to be compatible with new versions of external dependencies
(from pip, apt-get etc.). Any changes that follow are meant for internal
(from pip, apt-get , etc.). Any changes that follow are meant for internal
maintenance. We may use the OD API to release projects in the future,
in which case we will provide full install instructions or Docker images.
We encourage users seeking an actively maintained detection / segmentation
We encourage users seeking an actively maintained detection/segmentation
codebase to consider [TF-Vision](https://github.com/tensorflow/models/tree/master/official/vision)
or [scenic](https://github.com/google-research/scenic). We have preserved
the original install instructions below in case anyone wants to try out old
Expand All @@ -20,7 +20,7 @@ Creating accurate machine learning models capable of localizing and identifying
multiple objects in a single image remains a core challenge in computer vision.
The TensorFlow Object Detection API is an open source framework built on top of
TensorFlow that makes it easy to construct, train and deploy object detection
models. At Google we鈥檝e certainly found this codebase to be useful for our
models. At Google, we鈥檝e 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>
If you use the TensorFlow Object
Expand Down Expand Up @@ -91,7 +91,7 @@ We have released SpaghettiNet models optimized for the Edge TPU in the [Google T

SpaghettiNet models are derived from a TuNAS search space that incorporates
group convolution based [Inverted Bottleneck](https://arxiv.org/abs/1801.04381) blocks.
The backbone and detection head are connected through [MnasFPN](https://arxiv.org/abs/1912.01106)-style feature map
The backbone and detection head is connected through [MnasFPN](https://arxiv.org/abs/1912.01106)-style feature map
merging and searched jointly.

When compared to MobileDet-EdgeTPU, SpaghettiNet models achieve +2.2% mAP
Expand All @@ -100,7 +100,7 @@ used by MobileDet-EdgeTPU to achieve the same accuracy.

Sample config available [here](configs/tf1/ssd_spaghettinet_edgetpu_320x320_coco17_sync_4x4.config).

<b>Thanks to contributors</b>: Marie White, Hao Xu, Hanxiao Liu and Suyog Gupta.
<b>Thanks to contributors</b>: Marie White, Hao Xu, Hanxiao Liu , and Suyog Gupta.

### DeepMAC architecture

Expand Down Expand Up @@ -153,11 +153,11 @@ includes:
* 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
for distributed training --- our models are designed to be trainable using sync
multi-GPU and TPU platforms.
* Colabs demo鈥檌ng eager mode training and inference.

See our release blogpost [here](https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html).
See our release blog post [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鈥檛 worry, we鈥檝e
got you covered.

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