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https://github.com/myatmyintzuthin/aquarium_object_detection

Training an Aquarium Object Detection for underwater health monitoring using Tensorflow2 Object Detection API.
https://github.com/myatmyintzuthin/aquarium_object_detection

deep-learning object-detection tensorflow-object-detection-api underwater-object-detection

Last synced: 16 days ago
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Training an Aquarium Object Detection for underwater health monitoring using Tensorflow2 Object Detection API.

Host: GitHub
URL: https://github.com/myatmyintzuthin/aquarium_object_detection
Owner: myatmyintzuthin
License: mit
Created: 2021-10-07T05:12:10.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2021-10-30T04:31:32.000Z (about 3 years ago)
Last Synced: 2023-08-02T10:39:38.604Z (over 1 year ago)
Topics: deep-learning, object-detection, tensorflow-object-detection-api, underwater-object-detection
Language: Jupyter Notebook
Homepage:
Size: 22.7 MB
Stars: 1
Watchers: 1
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Aquarium Object Detection :tropical_fish:

[![TensorFlow 2.6](https://img.shields.io/badge/TensorFlow-2.6-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.6.0) 

Underwater Health Monitoring is an essential way to prevent extinction of sea animals and coral reef. In this repository, we will build an aquarium object detection system using Deep Learning and Computer Vision. 

![sample1.jpg](https://github.com/myatmyintzuthin/aquarium_object_detection/blob/main/assets/sample1.PNG)

![sample2.jpg](https://github.com/myatmyintzuthin/aquarium_object_detection/blob/main/assets/sample2.PNG)

------

#### Dataset

The [Aquarium Object Detection Dataset](https://public.roboflow.com/object-detection/aquarium) is collected by Brad Dwyer(Roboflow team) from two aquariums in the United States: The Henry Doorly Zoo in Omaha (October 16, 2020) and the National Aquarium in Baltimore (November 14, 2020). The dataset consists of 638 images splitted into train, test and validation data.

![train1.jpg](https://github.com/myatmyintzuthin/aquarium_object_detection/blob/main/assets/train1.jpg)

------

#### Model 

We will be using EfficientDet D0 model from [TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md). They provide a collection of detection models pre-trained on the [COCO 2017 dataset](https://cocodataset.org/).

| Model name  | Speed (ms)  | COCO mAP | Outputs |

| ----------- | ----------- | -------- | ------- |

| [EfficientDet D0 512x512](http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d0_coco17_tpu-32.tar.gz)      | 39       | 33.6 | Boxes |

------

#### Metrics

After training for 4300 steps:

```

{'Loss/classification_loss': 0.18720163,

 'Loss/localization_loss': 0.08831813,

 'Loss/regularization_loss': 0.04052207,

 'Loss/total_loss': 0.31604183,

 'learning_rate': 0.07999277}

```

Validation Detection Metrics:

```

Accumulating evaluation results...

DONE (t=0.19s).

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.324

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.626

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.298

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.022

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.226

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.443

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.178

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.374

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.449

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.063

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.372

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.554

```

#### Tensor Board Metrics

![tensorboard.jpg](https://github.com/myatmyintzuthin/aquarium_object_detection/blob/main/assets/tensorboard.PNG)

------

#### References

[Custom object detection in the browser using TensorFlow.js by Hugo Zanini](https://blog.tensorflow.org/2021/01/custom-object-detection-in-browser.html) \

[TensorFlow Object Detection API Tutorial](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/) \

[TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md)