wiki:object_detector_app_2
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wiki:object_detector_app_2 [2023/01/19 12:54] vizycam |
wiki:object_detector_app_2 [2023/01/19 13:32] (current) vizycam [Importing, Exporting, and sharing Object Detector projects] |
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| This will lead to a familiar Google authorization below. | This will lead to a familiar Google authorization below. | ||
| - | [{{wiki:image_1344.jpg}}] | + | [{{wiki:image_1344.jpg?400}}] |
| Choose the Google account associated with your Vizy, followed by clicking on **Allow**. | Choose the Google account associated with your Vizy, followed by clicking on **Allow**. | ||
| Line 194: | Line 194: | ||
| After navigating through these, Google Colab will get busy training your CNN. It will take several minutes depending on whether a server with GPU resources is available. The actual training takes place with the call to ''object_detector.create'' about halfway through the script. You can watch the detection loss (''det_loss'') decrease with each training epoch as shown below. It's learning! | After navigating through these, Google Colab will get busy training your CNN. It will take several minutes depending on whether a server with GPU resources is available. The actual training takes place with the call to ''object_detector.create'' about halfway through the script. You can watch the detection loss (''det_loss'') decrease with each training epoch as shown below. It's learning! | ||
| - | [{{wiki:image_1361.jpg?700}}] | + | [{{wiki:image_1361.jpg?850}}] |
| | | ||
| After it's done, it will copy the CNN model it just created back to Google Drive. This happens in the last script command (see below). The green check to the left of the command indicates that it was able to successfully create the CNN model and copy it. | After it's done, it will copy the CNN model it just created back to Google Drive. This happens in the last script command (see below). The green check to the left of the command indicates that it was able to successfully create the CNN model and copy it. | ||
| - | [{{wiki:image_1350.jpg?700}}] | + | [{{wiki:image_1350.jpg?850}}] |
| Congratulations! You're ready to test the CNN model you just created. | Congratulations! You're ready to test the CNN model you just created. | ||
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| From the **Detect** tab, all detections are logged, including the incorrect detections. You can bring up all past detections in the **Detections** tab to get a better look. | From the **Detect** tab, all detections are logged, including the incorrect detections. You can bring up all past detections in the **Detections** tab to get a better look. | ||
| - | [{{wiki:image_1354.jpg?700}}] | + | [{{wiki:image_1354.jpg?850}}] |
| As you can see, there are several incorrect detections. Clicking on any of the pictures brings up the picture dialog for that picture. | As you can see, there are several incorrect detections. Clicking on any of the pictures brings up the picture dialog for that picture. | ||
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| From here you can click on **Copy image to the training set**. It's recommended to go through all of the detections and copy all incorrect detections to the training set in this way. Next, switch to the **Training set** tab, and locate the copied images, which will appear on the last page as unlabeled images. | From here you can click on **Copy image to the training set**. It's recommended to go through all of the detections and copy all incorrect detections to the training set in this way. Next, switch to the **Training set** tab, and locate the copied images, which will appear on the last page as unlabeled images. | ||
| - | [{{wiki:image_1356.jpg?700}}] | + | [{{wiki:image_1356.jpg?850}}] |
| Go ahead and correctly label the images as we've done before. | Go ahead and correctly label the images as we've done before. | ||
| - | [{{wiki:image_1357.jpg?700}}] | + | [{{wiki:image_1357.jpg?850}}] |
| Line 249: | Line 249: | ||
| After you download the re-trained model, you will now have two model versions. In our example, the first version is ''rock paper scissors_01.tflite'' and the re-trained model is ''rock paper scissors_02.tflite''. The model versions simply increment in this way, so you can keep track and compare previous versions with newer ones. Along those lines, you can easily check to see if the re-trained model has improved by enabling **Test models** at the bottom of the **Training set** tab. When enabling **Test models**, it automatically selects the most recent version as the first model version (in this case ''rock paper scissors_02.tflite''). Selecting another model version in the 2nd dropdown allows you to do a simultaneous comparison to see how the two model versions behave. This is shown below where the boxes are red or green depending on whether the detection is version 02 or version 01, respectively. | After you download the re-trained model, you will now have two model versions. In our example, the first version is ''rock paper scissors_01.tflite'' and the re-trained model is ''rock paper scissors_02.tflite''. The model versions simply increment in this way, so you can keep track and compare previous versions with newer ones. Along those lines, you can easily check to see if the re-trained model has improved by enabling **Test models** at the bottom of the **Training set** tab. When enabling **Test models**, it automatically selects the most recent version as the first model version (in this case ''rock paper scissors_02.tflite''). Selecting another model version in the 2nd dropdown allows you to do a simultaneous comparison to see how the two model versions behave. This is shown below where the boxes are red or green depending on whether the detection is version 02 or version 01, respectively. | ||
| - | [{{wiki:image_1370.jpg?700}}] | + | [{{wiki:image_1370.jpg?850}}] |
| We can see that we improved (see below). (You can click on the individual pictures within the **Training set** tab to examine them more closely.) | We can see that we improved (see below). (You can click on the individual pictures within the **Training set** tab to examine them more closely.) | ||
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| - **Import album pictures into your Object Detector project**. This will copy the images in the Google Photos album into the training set of the currently open project. Start by selecting **Import photos...** from the **File** menu, then type the name of the album into the text box as shown below. \\ \\ {{wiki:image_1410.jpg?400}} \\ \\ After clicking on **Import**, Vizy will locate the album, retrieve the images, and add them to the end of the training set. So after it's done, go to the last page(s) of the training set to see the imported images. Bear in mind that the album name is case-sensitive. If Vizy has trouble finding the album, make sure you can see the album from the Google Photos page while logged in via Vizy's Google account. Once the images are imported, you can [[wiki:object_detector_app_3#labeling|label them]] as before. Easy-peasy! | - **Import album pictures into your Object Detector project**. This will copy the images in the Google Photos album into the training set of the currently open project. Start by selecting **Import photos...** from the **File** menu, then type the name of the album into the text box as shown below. \\ \\ {{wiki:image_1410.jpg?400}} \\ \\ After clicking on **Import**, Vizy will locate the album, retrieve the images, and add them to the end of the training set. So after it's done, go to the last page(s) of the training set to see the imported images. Bear in mind that the album name is case-sensitive. If Vizy has trouble finding the album, make sure you can see the album from the Google Photos page while logged in via Vizy's Google account. Once the images are imported, you can [[wiki:object_detector_app_3#labeling|label them]] as before. Easy-peasy! | ||
| - | ===== Importing, Exporting, and sharing Object Detector projects ===== | + | ===== Importing, exporting, and sharing Object Detector projects ===== |
| Through the powers of the Internet (and Google Drive), you can export your project and share your CNN efforts with others. And they can import your project to evaluate, improve, and share it back with you and possibly others. When exporting a project, all of the training set images, models, and settings are zipped up and uploaded to Google Drive. (Note, the detection images are not included when you export a project.) | Through the powers of the Internet (and Google Drive), you can export your project and share your CNN efforts with others. And they can import your project to evaluate, improve, and share it back with you and possibly others. When exporting a project, all of the training set images, models, and settings are zipped up and uploaded to Google Drive. (Note, the detection images are not included when you export a project.) | ||
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| {{wiki:image_1413.jpg?400}} | {{wiki:image_1413.jpg?400}} | ||
| - | Below is a share key for our rock paper scissors project. **Copy and paste it to give it a try on your Vizy!** | + | Below is a share key for our rock paper scissors project. **Copy and paste it to give it a try!** |
| ''VWyJPRFBHIiwgInJvY2sgcGFwZXIgc2Npc3NvcnMiLCAiMXpDc2c4aVlIVkpaSk53a0xIeTlPdmJvcjJKaWFjYURlIl0=V'' | ''VWyJPRFBHIiwgInJvY2sgcGFwZXIgc2Npc3NvcnMiLCAiMXpDc2c4aVlIVkpaSk53a0xIeTlPdmJvcjJKaWFjYURlIl0=V'' | ||
wiki/object_detector_app_2.1674154462.txt.gz ยท Last modified: 2023/01/19 12:54 by vizycam
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