I am running tensorboard on a pytorch lightning_logs folder containing some results. However, in the images tab, the batch of images produced are always at step=0 (see attached screenshot). How do I view images produced at a later step on tensorboard? I tried to modify the global_step variable in the image functions in env/lib/python3.7/site-packages/torch/utils/tensorboard/writer.py but this doesn't do anything. I have also attached the command I have run and the contents of the lightning_log folder, as I am unsure if the writer.py file I previously mentioned is the one being accessed.
Command: tensorboard --logdir experiments/lightning_logs/version_8744369/
Contents of lightning_logs/version_8744369/: events.out.tfevents.1594223100.gl1019.arc-ts.umich.edu.150337.0,
events.out.tfevents.1594223139.gl1019.arc-ts.umich.edu.150338.0,
hparams.yaml
tensorboard output screenshot
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I want to show several runs in tensorboard, but tensorboard starts and don't show any run. What is the problem?
My folder structure is like the following:
Trainingfolder
annotations
exported-models
images
models
cv300_v4_l2_higher
cv300_v4_l2_lower
pre-trained-models
Now, I want to start tensorboard to compare the two runs cv300_v4_l2_higher and cv300_v4_l2_lower.
I tried to start tensorboard with several commands in my Trainingfolder, but they don't show me my two runs:
tensorboard --logdir=models
tensorboard --logdir models
tensorboard --logdir=run1:models/cv300_v4_l2_higher,run2:models/cv300_v4_l2_higher
I hope you can help to solve the problem, why the runs aren't show in the tensorboard.
Thank you.
what am I doing: I've collected images for tensorflow object api retraining job, label them using labelImg application, further i've resize collected images to reduce training job time.
I guess labels generated for primary collected images are not corresponds to newly resized images, so is it any scripts how can I change previously generated images according to newly resized images. Thank you!
Usually one convert the XML generated by labelImg into a single csv, then this csv is converted into a tfrecord file which contain both the images and the annotations. During this convertion coordinates are stored as relative (percentage on image width/height), thus you don't need to recalcute them. I gues this is your case too.
I'm trying to run a deep learning code. I usually run in spyder win10. The images obtained thru matplotlib are saved using something like:
plt.savefig('./output/'+filename+'_'+str(num)+'.png',dpi=360)
Occasionally, I would run the code thru the anaconda prompt using:
python abc.py
In this case, the image would appear on-screen. I realise that if I enlarge them, I can get a much clearer image with more details, as compared to the saved image. Why is this so? I have attached some images for comparison
I uploaded a previously created Jupyter notebook. I could initially see all the cell outputs in Colab right after uploading it, but if I close the notebook and come back to it later -- or if I share the notebook with a coworker -- then all the cell outputs have been cleared, which is quite annoying.
This is happening even though I've verified that the following two checkboxes are UNCHECKED:
Edit > Notebook settings > Omit code cell output when saving this notebook
Tools > Preferences > New notebooks use private outputs (omit outputs when saving)
From what I can tell, it looks like the cell outputs get preserved across sessions for notebooks created and edited in Colab, but not for notebooks that were created elsewhere and then uploaded. What am I missing? How can I preserve cell outputs across sessions in uploaded notebooks?
Are you trying to open the file from Drive directly in Jupyter?
If so, you'll need to save the full file using the File -> Download ipynb menu item.
By default, Colab saves outputs using a different format to support incremental saves, so the Drive file created during auto-save will show outputs, but only in Colab itself, and you'll need to download the full ipynb to export to other notebook viewing tools.
please help me with training my own dataset on mask_rcnn_inception_resnet_v2_atrous_coco model.
https://github.com/tensorflow/models/tree/master/research/object_detection
model:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
I have refered to https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/instance_segmentation.md ; but I can't clearly understand the steps.
Do we have to give the Bounding box coordinates of the object along with the mask.png file?
How to convert the mask data to tfRecord files (for instance segmentation).?
Can anyone suggest the labelling tool used for bounding box as well as mask.png file!!
tools like LabelBox, labelme, labelimg gives either bounding box coordinated or mask.png file or the polygon coordinates for the object.
please help
The best you give png mask and xml labelization it should be working with create_pet_tf_record.py, set faces_only=false in this file... You can see into the code what is expected in this file..
change path into to point your directories in pipeline configuration
Do we have to give the Bounding box coordinates of the object along with the mask.png file?
Answer: Yes, you need the original images, bounding box files, and mask images.
Use the following tool to annotate each object in your original images Label image
Once you're done with this, you need to annotate each pixel inside each bounding box. There are several tools you can use, for example you can use these tool VGG annotator