Training images with zero annotations

[NiklasWilson]

What would be the best way to modify the code to support images with zero annotations?
When you export the annotations as csv from Vott it makes a new folder with All the images you checked but it leaves images without annotations absent from the csv. Which means the training script will simply ignore them.

I was thinking the "Convert_to_YOLO_format.py" would be the best place to add in the images without annotations by making it check the folder for all the images that exist and than checking to see if they are referenced in the csv and if not add a row without bounding boxes.

Do you think this would be the best way? or will the training script fail if an images with no bounding boxes is provided in the dataset?

[AntonMu]

Hi Niklas - I think we would need to make sure that they are explicitly labeled as not containing the object. Because what will happen more often than not is that there are 1000s of images in the image folder and after labeling a few hundred, someone might stop labeling and then the way you describe it, the program would automatically assume that all remaining images don't have the object.

What about making an extra folder somewhere called "No_Object" in Training_Images and then simply add all files in there to the data_train.txt the way you described it.

[NiklasWilson]

Adding a "No_object" folder would be good however by default VoTT puts the annotated csv as well the images that have been looked at into the csv_export folder. (By default it excludes images that have yet to be checked for tags)

It could be a lot of extra labor to manually move images to a "no_object" folder as I wont know they have no object till after I look at them in VoTT. I am thinking a flag could be added to the script that defaults to false like --check_for_no_annotations or something similiar/shorter.

Adding a no object folder as well that is checked by default would be cool though.

[NiklasWilson]

Ok the folder path actually will work for both of us. I can just pass in the folder where the annotations are. PR coming up with the change, by default it will now check for a no_object folder.

[AntonMu]

Ok cool - thanks for this!

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On Mon, Nov 25, 2019 at 11:20 Niklas Wilson ***@***.***> wrote: Ok the folder path actually will work for both of us. I can just pass in the folder where the annotations are. PR coming up with the change, by default it will now check for a no_object folder. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#43?email_source=notifications&email_token=AEEZVCXIVCX663N6PTQKJN3QVQQOZA5CNFSM4JRMCLDKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEFDQCKY#issuecomment-558301483>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEEZVCVTV6FPTJNGMFKJ6BDQVQQOZANCNFSM4JRMCLDA> .

[NiklasWilson]

No problem. Check that out, obviously make sure it doesn't cause you any issues :)

It looks like it includes the last PR? Im not sure how to update a fork to resolve this, ive actually never done a PR across a fork before.

[forceassaulter]

@NiklasWilson How to do the image training with zero annotation, because I just want the machine to classify whether the object present in the image or not, bounding box is unnecessary.

[AntonMu]

Hi @forceassaulter - there is some confusion here. @NiklasWilson is talking about improving the object detector by adding additional images without annotations. What you are looking for is image classification. That's a different algorithm. Check out Inception v3 for instance.

[forceassaulter]

Any good example for that?

[NiklasWilson]

@forceassaulter : @AntonMu Is correct there is another algorithm used for classification instead of detection. However if you willing to draw annotations around stuff, there is no reason why you cant use this code to do both, in the detection script instead of drawing boxes on images just use was it detected to classify the image. That said I believe there is a huge accuracy/efficiency loss if you were to use this algorithm for pure image classification.

My additional code to accept images with no drawn boxes/annotations is not the solution you want though. It works along side images with annotations.

[forceassaulter]

@NiklasWilson Other than image classification, I also need an algorithm to draw bounding boxes on object detected, so I was thinking to determine whether there is box drawn on image to determine whether the object is in the image.

[AntonMu]

Ok if your goal is to detect objects in images then yolo is the right algorithm for you. This algorithm (described here) figures out if an object is in an image and then draws a bounding box around it. You can simply train on multiple classes and everything should work fine.

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On Thu, Dec 5, 2019 at 17:38 forceassaulter ***@***.***> wrote: @NiklasWilson <https://github.com/NiklasWilson> Other than image classification, I also need an algorithm to draw bounding boxes on object detected, so I was thinking to determine whether there is box drawn on image to determine whether the object is in the image. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#43?email_source=notifications&email_token=AEEZVCVFILGIOJ66C4F77DLQXGUH7A5CNFSM4JRMCLDKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEGCXJOY#issuecomment-562394299>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEEZVCRBHCA4IWZCAN7FKO3QXGUH7ANCNFSM4JRMCLDA> .

[NiklasWilson]

Yes. The drawing of the boxes is actually optional, afters it trained when you run the detection code it give you back a list/array of detections with the class, box coordinates and percentage of certainty. By default it draws every box but you could use this data to "classify" the image and draw boxes around some or all of the detections as you see fit.

[NiklasWilson]

I personally have modified the detection code to run on live camera footage, with frame skipping that draws the same box across multiple changing frames aka the footage stays real time while the detection only updates every few frames.