According to information on the Nvidia website Digits uses datatasets in Kitti format. Is there possibilty in Digits or in external application to prepare such dataset or I will have to write it on my own?
I would like to simply draw bounding boxes on the displayed image and then have it converted to txt appropiate txt file.
Thanks in advance!
Yep, you can use one of the available solutions for bounding box annotations eg. RectLabel, save the annotations in Pascal VOC format and then transform it to Kitti using one of the freely available converters, eg: VOD Converter
Related
does anybody know a reliable way to convert charts to a dataset (csv, pandas, ...)? I only have the png of the chart below but need the dataset to plot the chart on my own.
Thanks in advance!
You could try this web based tool to extract numerical data from plot images: WebPlotDigitizer. Tool allows you to upload images with plots/graphs, select data points of interest and download them into csv format.
You could also install it on your desktop: WebPlotDigitizer
My YOLOv2Tiny model is drawing bounding boxes in the correct place, however consistently smaller than the object. This got me wondering if I correctly annotated the images.
Does YOLOv2 use the Pascal VOC .xml format or the same YOLO .txt format that YOLOv4 uses?
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.
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
I am building a RCNN detection network using Tensorflow's object detection API.
My goal is to detect bounding boxes for animals in outdoor videos. Most frames do not have animals and are just of dynamic backgrounds.
Most tutorials focus on training custom labels, but make no mention of negative training samples. How do these class of detectors deal with images which do not contain objects of interest? Does it just output a low probability, or will it force to try to draw a bounding box within an image?
My current plan is to use traditional background subtraction in opencv to generate potential frames and pass them to a trained network. Should I also include a class of 'background' bounding boxes as 'negative data'?
The final option would be to use opencv for background subtraction, RCNN to generate bounding boxes, then a classification model of crops to identify animals versus background.
In general it's not necessary to explicitly include "negative images". What happens in these detection models is that they use the parts of the image that don't belong to the annotated objects as negatives.
If you expect your model to differentiate between "found a figure" and "no figure", then you will almost certainly need to train it on negative examples. Label these as "no image". In the "no image" case, yes, use the entire image as the bounding box; don't suggest that the model recognize anything smaller.
In "no image" cases, you may get a smaller bounding box, but that doesn't matter: in inference, you'll simply ignore whatever box is returned for "no image".
Of course, the critical issue here is to try it out, and see how well it works for you.
I have found success by scanning my ground truth, copying the box areas plus a margin, then pasting tilings of those box areas onto new background images (guaranteed to have no objects), and creating corresponding XML files with the box category assertions.
I collect non-objects as "uncategorised" boxes - usually from glitches in the output from my latest model. These are tiled (just like the "is-objects") but are not updated in the XML files.
I produce tilings at various scales to build each new training set.
A further explanation and sample python code is here:
https://github.com/brentcroft/ground-truth-productions