thank you so much for the very nice video. you've put a lot of effort into making it clear, even for newbies. explaining step by step run-length encoding with a very nice example like that... kudos to you!
Slight correction on the RLE Segmentation part: The RLE does not count pixels from the top left corner in row-major order but rather in column-major order. That means you don't count the pixels to the right side of the image but to the bottom of the image.
Thanks Douglas! The course came out about a year ago actually and we're coming up on 500 students. 😀 www.immersivelimit.com/course/creating-coco-datasets
Very clear explanation man, thanks. Also i've seen the playlist of "rendering dataset in blender" on your channel, i've dan a quick look you a're training model there is it COCO dataset that you use in that playlist? I couldn't find out it yet.
Thanks for the video! One question, does the file name in the image segment has to be the relativ path from the json file to the image, like "/.jpg" or is only the filename needed?
It should just be “.jpg”. Whatever code is consuming the JSON is required to know the folder path. Every neural net I’ve seen does this a little differently unfortunately, so you may have to dig into the code.
Excellent video! Quick question, what about Object Tracking? Are they expected to be custom attributes? or is there a standard COCO format for object IDs as well?
Thank you very much! I was wondering which annotation tool you are using? I hope you are still seeing this! I've been looking for an open source annotation editor where I can input already existent RLE in coco JSON format.
I believe in this one I was just using examples from the COCO dataset. For my own datasets, I don’t create them manually, so I don’t have a tool I can recommend
Hello, I want to convert a COCO dataset annotation to a binary mask. The problem I have is that after conversion with pycocotools method annToMask nearly all the labels have the same colour and the segmentation network recognises it as one label. Is there a fix to that so that every label has a different colour. Please help
insightful, thanks. how do i merged the COCO annotations for trucks and persons with your custom annotations for forklifts? The COCO database doesnt have forklifts, so i have annotated the forklifts in images with persons and trucks. but COCO has trucks and person, so i want to merge that.
On the "area" that is found in annotations, I don't understand how it can be a representation of number of pixels that are shaded in the object. I mean, I have noticed decimal values like 702.105 or 2049.9325 when I am expecting integer values for "number of pixels"....how would 0.105 of a pixel be represented?
I oversimplified it slightly with my explanation. It's technically the area inside the polygon. Even if polygon vertices were all on exact pixels (in COCO they're not), fractional areas would be possible. For example, if you had a right-triangle with points (0,0), (5, 0), (0, 5), the area would be 12.5. Hope that helps!
@@ImmersiveLimit thanks a lot. It now makes perfect sense as an actual area than "number of". Your vertices example actually clears my doubts completely! I am following your material closely and I am getting a lot of help! Thanks!
Referring back to your explanation of RLE, would you then be structuring the value of the "counts" key in terms of semantic segmentation rather than instance segmentation? Because in your apple example, the mask cant differentiate the instances of the 4 apples from the mask. Does this apply to the elephants too? (because I could still see the instances on the elephant picture) Thank you!
In the official COCO dataset they are not consistent. I think in a perfect case every instance would be separate, but because humans were annotating a lot of images and probably being paid per image completed, we have some cases where they just got lazy. The elephant one is a strange case where they did all but one elephant as a separate instance. Maybe the annotation tool had a limit?
Hey, thanks for your perfect explanation! I can't figure out if coco formatting supports time series in any way? All I can think of is comparing coco["images"][idx]["date_captured"], coco["images"][idy]["date_captured"], or maybe setting all images in the same time-window (idx) with different "file_names" e.x ..[idx]["file_name_t0"], .., [idx]["file_name_tn"]?
Thank you for the compliment! I think you are on the right track. The official COCO project doesn’t support it, so you would have to add your own. You could add a frame number or something to the image data in the annotations json and then just consume that with your training algorithm.
@@ImmersiveLimit Yes, figured so.. Do you have in mind any deep learning time-series repo that utilizes COCO formatting scheme? I could definitely use something ready like Detectron2, but RNN based. Again, thanks!
Excellent video! "The course is live! You can get a deal on the course and support me with this coupon" I can't find any coupon! Please comments. Does it include only annotation for segmentation and bounding box or does it include also keypoint and panoptic? Thanks!
Yes. It’s useful to know that the polygons are converted to image masks during training, so any polygon or set of polygons that fully covers the object will work. A single annotation can have multiple polygons, so if you were annotating a donut 🍩 you could think of it like cutting the donut in half and creating two polygons, one for each side.
@@ImmersiveLimit Thanks for the quick response! But I still getting a "false" binary image. In my case there is only one annotation (so only one array with coordionates). The annotation has mutiple holes in it. When I'm trying to convert the annotation with "annToMask" I still get a binary image where the holes are also filled with the same color as the annotation...
Awesome video. I was wondering about the coco_url. My understanding is that this is the image location where the model should look in. What if I want to create the annotations for a dataset I have on my personal computer, how would the model know where to look for the image? How would I write that in the annotations?
@@ImmersiveLimit okay, I understand. But still, how would the model know where to look for images that I have on my computer? Is there a way to specify that in the coco format?
Correct. The trainer is what looks for images, and in every case I’ve seen, you have to download the dataset to your computer. It would be too slow to fetch them all from the web.
For now, I recommend checking out github.com/jsbroks/coco-annotator. Unfortunately it takes a lot of time to do this for hundreds of images, but I’m close to launching a Udemy course that can help. Stay tuned for that!
Is there any difference between annotating my images for object-detection-api using Labellmg . and using the COCO Dataset JSON Format. I saw on your blog post, how you converted foreground and background images to 11k Training and validation data-set. I really like to achieve the same result. Can I convert the JSON file to xml..?
@@ImmersiveLimit sorry my question was wrong, the question is when I am converting coco data using json.dumps(data, indent=4), the segmentation lists are coming in vertical format, and taking a lot of space. How you converted them to horizontal
Hello, thank you for your video. I have one question about the segmentation part. If we have an object with a hole like a cup handle for example. How to specify that the hole need to be empty ? I mean, we will have two contours and the hole contour area is empty. How represent this case? (Hope the question is clear, sorry English is not my language)
All contours are eventually converted to pixel masks, so you can either do RLE format or you could have two contours for an object, for example one of the cup, one of the handle. The result should be the same.
Is this the required format for the annotations to work with matterport/mask R-CNN repo ? I think the only accepted format for this repository is the one got with vgg annotator where there are regions, regions_attributes in the json file annotations ... maybe it is a stupid question but please can someone answer me ?
Check out this page on my website, I explain the different types of annotations, including captions. www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch
In the instances_val2017.json there are links to the images and there are annotations for that images stored separetely. How can i put things toghever like you did in the video? I mean combine raw image and annotation.
You have to look for annotations that match the image id. In my example I did it manually, but you could do it with code. Does that answer your question?
hı, ı have a dataset and annotion file, json format. json file include object location. but ı dont know import my deep learning module. ı can import images but cant label. help please
@@ImmersiveLimit yes all courses use coco,mnist,cifar etc. dataset, or use tensorflow object detection apı. but ı want to prepare own dataset and use my model. but my label files include box location etc. ı cant import. ı took your udemy course, can ı use your course for this problem
@@ImmersiveLimit You were able to get the segmentation of the instances of an image in the code (x and y) regardless if they are an iscrowd (sequence coding method or not), but these are the train/validation images annotations, can we find out the segmentation or the instance coordinates ( x and y points) of the test images outputs of regions of interest lets us say a car
Oh, I think I understand. So, the output of your neural net (Mask R CNN for example), would give you a pixel mask that will have imperfections. You would want to use scikit-image and shapely to create your list of points. I do something similar in create_coco_annotations() here github.com/akTwelve/cocosynth/blob/master/python/coco_json_utils.py, however I don’t have to clean up the mask first. You’d have to figure that bit out for yourself.
Do you just want the same categories but less images of each? If so, you just have to load the JSON with Python (like I do) and remove some of the images from the list. Area is how many pixels are in the mask.
@@ImmersiveLimit No i mean resize each image to lets say 550×550. If i resize the images i have to change the points inside the annotation.json file. Is there a script or tool to do this? Anyway in the end i manually created a script for resizing labelme data since i used labelme for the annotations. The i converted using labelme2coco.py script. But a script made for resizing coco style annotations and images would be nice.
Got it, I think typically resizing is done at training or inference time automatically rather than modifying the dataset, but thanks for the suggestion.
@@ImmersiveLimit I was trying to use yolact in my case. I am new to yolact so i had to do it manually. Make a tutorial aboit yolact if u can! Thanks you
Thank you for the Jupyter notebook! I made a fork of your gist and added the keypoint visualization. github.com/chitoku/coco_viewer/blob/master/COCO_Image_Viewer.ipynb
آپ کے ایک کلک سے میری غریب زندگی بدل سکتی ہے اللہ پاک آپ کو دنیا کی تمام خوشیان عطا فرمائے اور آپ کے والدین کا سایہ تا دیر تک آپ پر قائم رہے آمین ثمّ آمین 🙏🙏🙏🙏mgoos
آپ کے ایک کلک سے میری غریب زندگی بدل سکتی ہے اللہ پاک آپ کو دنیا کی تمام خوشیان عطا فرمائے اور آپ کے والدین کا سایہ تا دیر تک آپ پر قائم رہے آمین ثمّ آمین 🙏🙏🙏🙏good pileeeez
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Hey man, you have no idea, how much you've helped me. I hope good things happen to you. Thank you so much for doing what you do.
That's awesome to hear, you're welcome!
Man, what a great video. Clear, well-edited, informative and to-the-point. Thank you for that!
Thank you, Tunai! My wife (and newly minted video editor) was thrilled to hear the "well edited" part. 😊
Best explanation about COCO dataset I've ever seen, thank you!
Wow, run length encoding for the maskings is so clever
Great video! although i do recommend people watching to use 1.5x or 2.0x speed, considering this is still easy to follow at that speed.
thats what i did
That RLE stuff was good. Good work.
Masterfully done, my lad! Thank you so much for explaining this!
Thanks for your comprehensive video. It was very helpful.🙏
Thanks!!!! I would love to see a reference on the COCO page to this video. I've just spent 2 days trying to decipher this.
Glad I wasn't the only one :D
thank you so much for the very nice video. you've put a lot of effort into making it clear, even for newbies. explaining step by step run-length encoding with a very nice example like that... kudos to you!
Simple and extremely useful video. Big Thanks
Hi Adam thanks for your video and your post, I´m trying to implement the Mask RCNN algorithm, and your video is helping me so much. Tanks so much!
Glad it was helpful!
Great job Adam ! Very helpful of you and i hope you are rewarded :)
I'm happy it was helpful!
This is exactly what I needed. Thanks man, you are life savior.
Thank you for such an enlightening video
Wow very nice video!! It really pinpoints all the confusions and complexities related to COCO. Thank you!!
no words to say very very tanks bro u saved my life :)))
Slight correction on the RLE Segmentation part: The RLE does not count pixels from the top left corner in row-major order but rather in column-major order. That means you don't count the pixels to the right side of the image but to the bottom of the image.
do you know why? Wouldn't this result in a bad cache access pattern?
thank you so much you saved me so much time you are a great person i hope great things happen to you
What a nice video !
I hope the course works out, man. Good stuff.
Thanks Douglas! The course came out about a year ago actually and we're coming up on 500 students. 😀 www.immersivelimit.com/course/creating-coco-datasets
Thanks very helpful. You helped with my subject.
Very clear explanation man, thanks. Also i've seen the playlist of "rendering dataset in blender" on your channel, i've dan a quick look you a're training model there is it COCO dataset that you use in that playlist? I couldn't find out it yet.
man you saved my life
cheers bro, awesome explanation and visualisation, quite logical and tidy!
Thanks a lot, you solved my problem😉
This is incredible job. It helped me a lot! Thanks Adam.
Thank you!
Great video. Extremely helpful. Thankyou
very comprehensive explain,thanks a lot
Great video man. Thank you so much!
You really helped me a lot! Thank you!
superb man ... this is such a wonderful explanation of the coco dataset
Thank you!
Thank you for your excellent video. It does help me a lot.
Great explanation !!
Hey, Thanks for the video. I appreciate your effort. Is there a script available that convert COCO (with RLE) to yolo format?
Ok so how do we actually convert a folder of images into these json files?
Thanks for the video! One question, does the file name in the image segment has to be the relativ path from the json file to the image, like "/.jpg" or is only the filename needed?
It should just be “.jpg”. Whatever code is consuming the JSON is required to know the folder path. Every neural net I’ve seen does this a little differently unfortunately, so you may have to dig into the code.
Thank you very much!! It helps me a lot!
Thank you so much! The video is great, you helped me a lot.
thank you so much, it helped me alot
It was very helpful thank you very much! and I want to know how to visualise this data?
Excellent video! Quick question, what about Object Tracking? Are they expected to be custom attributes? or is there a standard COCO format for object IDs as well?
Thank you very much! I was wondering which annotation tool you are using? I hope you are still seeing this! I've been looking for an open source annotation editor where I can input already existent RLE in coco JSON format.
I believe in this one I was just using examples from the COCO dataset. For my own datasets, I don’t create them manually, so I don’t have a tool I can recommend
Hello,
I want to convert a COCO dataset annotation to a binary mask. The problem
I have is that after conversion with pycocotools method annToMask nearly all the labels have the same colour and the segmentation network recognises it as one label. Is there a fix to that so that every label has a different colour. Please help
insightful, thanks. how do i merged the COCO annotations for trucks and persons with your custom annotations for forklifts? The COCO database doesnt have forklifts, so i have annotated the forklifts in images with persons and trucks. but COCO has trucks and person, so i want to merge that.
You’ll need to write code (I would use Python) to open up the annotation json and add the new items.
On the "area" that is found in annotations, I don't understand how it can be a representation of number of pixels that are shaded in the object. I mean, I have noticed decimal values like 702.105 or 2049.9325 when I am expecting integer values for "number of pixels"....how would 0.105 of a pixel be represented?
I oversimplified it slightly with my explanation. It's technically the area inside the polygon. Even if polygon vertices were all on exact pixels (in COCO they're not), fractional areas would be possible. For example, if you had a right-triangle with points (0,0), (5, 0), (0, 5), the area would be 12.5. Hope that helps!
@@ImmersiveLimit thanks a lot. It now makes perfect sense as an actual area than "number of". Your vertices example actually clears my doubts completely! I am following your material closely and I am getting a lot of help! Thanks!
Awesome, glad I could help, Jasper. 🙂
Thank you very much!!!
Referring back to your explanation of RLE, would you then be structuring the value of the "counts" key in terms of semantic segmentation rather than instance segmentation? Because in your apple example, the mask cant differentiate the instances of the 4 apples from the mask. Does this apply to the elephants too? (because I could still see the instances on the elephant picture) Thank you!
In the official COCO dataset they are not consistent. I think in a perfect case every instance would be separate, but because humans were annotating a lot of images and probably being paid per image completed, we have some cases where they just got lazy. The elephant one is a strange case where they did all but one elephant as a separate instance. Maybe the annotation tool had a limit?
Hey, thanks for your perfect explanation! I can't figure out if coco formatting supports time series in any way? All I can think of is comparing coco["images"][idx]["date_captured"], coco["images"][idy]["date_captured"], or maybe setting all images in the same time-window (idx) with different "file_names" e.x ..[idx]["file_name_t0"], .., [idx]["file_name_tn"]?
Thank you for the compliment! I think you are on the right track. The official COCO project doesn’t support it, so you would have to add your own. You could add a frame number or something to the image data in the annotations json and then just consume that with your training algorithm.
@@ImmersiveLimit Yes, figured so.. Do you have in mind any deep learning time-series repo that utilizes COCO formatting scheme? I could definitely use something ready like Detectron2, but RNN based. Again, thanks!
I haven't looked for any, sorry!
@@ImmersiveLimit Alright, farewell!
Thank you very much !!
Excellent video!
"The course is live! You can get a deal on the course and support me with this coupon"
I can't find any coupon! Please comments.
Does it include only annotation for segmentation and bounding box or does it include also keypoint and panoptic?
Thanks!
Sorry I didn’t see this right away, but it looks like you already bought the course. Thank you!
Great! thanks man!
really really clear, thank you a lot
Very helpful, thanks
excellent! very helpful
Thanks, glad to help!
thank you very much!
Thx for your video bruhh!
Nice video!
Is there an option to make holes in polygon annoatations?
Yes. It’s useful to know that the polygons are converted to image masks during training, so any polygon or set of polygons that fully covers the object will work. A single annotation can have multiple polygons, so if you were annotating a donut 🍩 you could think of it like cutting the donut in half and creating two polygons, one for each side.
@@ImmersiveLimit Thanks for the quick response!
But I still getting a "false" binary image.
In my case there is only one annotation (so only one array with coordionates). The annotation has mutiple holes in it.
When I'm trying to convert the annotation with "annToMask" I still get a binary image where the holes are also filled with the same color as the annotation...
Awesome video. I was wondering about the coco_url. My understanding is that this is the image location where the model should look in. What if I want to create the annotations for a dataset I have on my personal computer, how would the model know where to look for the image? How would I write that in the annotations?
In that case just leave coco_url out. None of the neural net libraries I’ve used actually use it. I think it’s mostly for reference.
@@ImmersiveLimit okay, I understand. But still, how would the model know where to look for images that I have on my computer? Is there a way to specify that in the coco format?
You’ll have to read the code to see where it loads the images. It probably has a config variable the points to a local directory.
@@ImmersiveLimit I see. So that would be more dependent on the model training process than the dataset creation process?
Correct. The trainer is what looks for images, and in every case I’ve seen, you have to download the dataset to your computer. It would be too slow to fetch them all from the web.
If i want to use this course to create my own datasets, will it work on yolact++, or just on Mask R-Cnn
I’ve used it for both and it worked well. Same COCO dataset format, just different configuration, obviously. 🙂
Hi , tks for your kind explanation and i have a one question.
How can i get the segmentation from my own image?? should i use some annotation tools?
For now, I recommend checking out github.com/jsbroks/coco-annotator. Unfortunately it takes a lot of time to do this for hundreds of images, but I’m close to launching a Udemy course that can help. Stay tuned for that!
Immersive Limit tks alot i will follow up:)
for tensorflow facelandmark / keypoint dataset, which dataset does it use?
Is there any difference between annotating my images for object-detection-api using Labellmg . and using the COCO Dataset JSON Format.
I saw on your blog post, how you converted foreground and background images to 11k Training and validation data-set. I really like to achieve the same result. Can I convert the JSON file to xml..?
I’m not familiar with the other format, but you could most likely write a custom Python script to do the conversion or find one on GitHub
I have annotations in the text files. How should I proceed?
hey brother loved this explanation, can you tell me how you converted this coco data format into json data format?
Coco format is already in json, I didn’t convert it
@@ImmersiveLimit sorry my question was wrong, the question is when I am converting coco data using json.dumps(data, indent=4), the segmentation lists are coming in vertical format, and taking a lot of space. How you converted them to horizontal
First of all, GREAT job!! Thank you sooooooooooo much. BTW, is it possible to share the sample_annotations.json file with us?
I managed to dig it up. I thought I might have lost it! gist.github.com/akTwelve/c7039506130be7c0ad340e9e862b78d9
Hello, thank you for your video. I have one question about the segmentation part. If we have an object with a hole like a cup handle for example. How to specify that the hole need to be empty ? I mean, we will have two contours and the hole contour area is empty. How represent this case?
(Hope the question is clear, sorry English is not my language)
All contours are eventually converted to pixel masks, so you can either do RLE format or you could have two contours for an object, for example one of the cup, one of the handle. The result should be the same.
@@ImmersiveLimit thank you for your answer I will try
how we calc. the segmentation area please and many thanks for ur effort
Count the number of pixels in the segmentation.
What data labelling software was used for the image masking/annotations before being exported as a json file?
You can read the COCO website for info on that, I think it’s on GitHub somewhere.
you are so kind thankyou thankyou
Thanks!
thank you , sir!!
You're welcome!
hey I unable to download coco dataset from the website.. please help me
Hello, can you please make a video about textvqa ?
Is this the required format for the annotations to work with matterport/mask R-CNN repo ? I think the only accepted format for this repository is the one got with vgg annotator where there are regions, regions_attributes in the json file annotations ... maybe it is a stupid question but please can someone answer me ?
Yes it is. You can check out the tutorials on our website, we have a couple that may help. www.immersivelimit.com
@@ImmersiveLimit Thak you very much, I found the right tutorial for my task.
Hi. What is the difference between the captions_train2017.json file and the instances_train2017.json file? (or instances_val2017.json file)
Check out this page on my website, I explain the different types of annotations, including captions. www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch
Please zoom in on vscode, otherwise great video you tought me a lot
In the instances_val2017.json there are links to the images and there are annotations for that images stored separetely. How can i put things toghever like you did in the video? I mean combine raw image and annotation.
You have to look for annotations that match the image id. In my example I did it manually, but you could do it with code. Does that answer your question?
which software or application had you used in this tutorial to perform annotations?
I didn't create these annotations. You might want to look at this tool though: github.com/jsbroks/coco-annotator
does id in category start from 0 or 1, do we have background category too?
I believe it starts at 1 because 0 is “background”
Does annotations[category_id] coincide with the coco.names file?
I'm not sure what the coco.names file is, but category_id should be consistent with whatever is in the categories section
Can I use this dataset for Object classification? How can I use it if there are two object categories?
No, you cannot use it for classification.
Perfect
what does id in annotation represent? should the id in annotation
be unique among the dataset?
Just unique among annotations.
sir what is dice coefficient for this?
Damn Bet I can finally code a labeled sorter
hı, ı have a dataset and annotion file, json format. json file include object location. but ı dont know import my deep learning module. ı can import images but cant label. help please
Hi, have you taken any courses on deep learning and computer vision?
@@ImmersiveLimit yes all courses use coco,mnist,cifar etc. dataset, or use tensorflow object detection apı. but ı want to prepare own dataset and use my model.
but my label files include box location etc. ı cant import. ı took your udemy course, can ı use your course for this problem
Can we find out the segmentation of the test images annotation (meaning the output coordinates)?
Sorry, I don’t understand your question. Can you clarify?
@@ImmersiveLimit You were able to get the segmentation of the instances of an image in the code (x and y) regardless if they are an iscrowd (sequence coding method or not), but these are the train/validation images annotations, can we find out the segmentation or the instance coordinates ( x and y points) of the test images outputs of regions of interest lets us say a car
Let us say we tested our image, can we find out the x and y coordinates of the output or not
Oh, I think I understand. So, the output of your neural net (Mask R CNN for example), would give you a pixel mask that will have imperfections. You would want to use scikit-image and shapely to create your list of points. I do something similar in create_coco_annotations() here github.com/akTwelve/cocosynth/blob/master/python/coco_json_utils.py, however I don’t have to clean up the mask first. You’d have to figure that bit out for yourself.
@@ImmersiveLimit Thank you a lot, so now I can extract the test images coordinates (x and y points ) and use the segmentation.
Have you converted COCO-JSON to Darknet-YOLO format?
I tried at one point, but it was so much work to get Darknet-YOLO set up for training that I gave up.
github.com/qqwweee/keras-yolo3 . You can run ' python coco_annotation.py ' to convert it
thanks
Hi pleas how can i resize an already labeled dataset? Plus what is area?
Do you just want the same categories but less images of each? If so, you just have to load the JSON with Python (like I do) and remove some of the images from the list. Area is how many pixels are in the mask.
@@ImmersiveLimit No i mean resize each image to lets say 550×550. If i resize the images i have to change the points inside the annotation.json file. Is there a script or tool to do this? Anyway in the end i manually created a script for resizing labelme data since i used labelme for the annotations. The i converted using labelme2coco.py script. But a script made for resizing coco style annotations and images would be nice.
Got it, I think typically resizing is done at training or inference time automatically rather than modifying the dataset, but thanks for the suggestion.
@@ImmersiveLimit I was trying to use yolact in my case. I am new to yolact so i had to do it manually. Make a tutorial aboit yolact if u can! Thanks you
We do have two YOLACT tutorials on our website, just not on TH-cam
Thank you for the Jupyter notebook!
I made a fork of your gist and added the keypoint visualization.
github.com/chitoku/coco_viewer/blob/master/COCO_Image_Viewer.ipynb
That’s awesome, nice work! I’ll take a look when I get a chance
nice
I am surprised that bicycles in COCO dataset are annotated in such a rough way....
They didn’t pay the annotators enough for that level of detail is my guess
where are captions with respect to images ?
They are in a different JSON file that includes an image id, caption id, and caption.
آپ کے ایک کلک سے میری غریب زندگی بدل سکتی ہے اللہ پاک آپ کو دنیا کی تمام خوشیان عطا فرمائے اور آپ کے والدین کا سایہ تا دیر تک آپ پر قائم رہے آمین ثمّ آمین 🙏🙏🙏🙏mgoos
How to download COCO DATASET for windows
cocodataset.org/#download
Can you explain how to apply 'ignore_regions' to our image?
I'm not sure what you mean, is that part of the COCO dataset?
Too bad these guys can't create a smaller, unique YOLO model for each category. Most of us just want to detect humans.
I see the value for sure, but I’m not sure it would work as well with a smaller model.
آپ کے ایک کلک سے میری غریب زندگی بدل سکتی ہے اللہ پاک آپ کو دنیا کی تمام خوشیان عطا فرمائے اور آپ کے والدین کا سایہ تا دیر تک آپ پر قائم رہے آمین ثمّ آمین 🙏🙏🙏🙏good pileeeez