Flutter Landmark Classifier (TFLite) - SpeedCode

Publicado el: 13 febrero 2021
en el canal de: puzzleleaf

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This is a project that can recognize and search landmarks in each region.
(Asia, South America, North America, Europe, Oceania, Antarctica)

It includes how to apply the model and how to process label data.

Unfortunately, the performance of the tflite model results worse than the example of tfhub.
To be used in real-world projects, you need to improve the performance of your model.

If you are in an iOS environment, you must add the following in Podfile:
https://github.com/PuzzleLeaf/flutter...

▶ Label Data Edit Page
https://onlinecsvtools.com/delete-csv...

▶ Model Info
TFHub Landmark Models
https://tfhub.dev/google/on_device_vi...
https://tfhub.dev/google/on_device_vi...
https://tfhub.dev/google/on_device_vi...
https://tfhub.dev/google/on_device_vi...
https://tfhub.dev/google/on_device_vi...

▶SourceCode
Github
https://github.com/PuzzleLeaf/flutter...

▶Packages
multi_image_picker
https://pub.dev/packages/multi_image_...

image
https://pub.dev/packages/image

tflite_flutter
https://pub.dev/packages/tflite_flutter

tflite_flutter_helper
https://pub.dev/packages/tflite_flutt...

url_launcher
https://pub.dev/packages/url_launcher

▶ Keyword
Classification, Landmark, Flutter, Tensorflow, TFLite

▶Timestamp
00:00 - Intro
00:44 - Project Setting
01:01 - AndroidManifest Setting
01:08 - Podfile Setting (TFLite Setting)
01:21 - Info.plist Setting
01:30 - TFLite Model & Label Setting
02:12 - Home Page
10:28 - ImageService
11:30 - ClassificationService
18:45 - Classification Page

#flutter #tflite #classification

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