Building Computer Vision Datasets in Coco Format
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- Опубликовано: 30 июн 2024
- Chapters:
0:00 Intro
1:01 What is computer vision?
1:23 Coco Datasets
2:13 Understanding CV use case: Airbnb Amenity detection
4:04 Datatorch Annotation tool
4:37 Tutorial to build Computer vision Coco dataset using Datatorch
12:25 Export CV Datasets as Coco Format
13:10 Understanding Coco Format
18:52 Outro
Links:
Datatorch: datatorch.io/
Medium Post:
Blog Post: anujsyal.com/building-compute...
Dataset Link: www.kaggle.com/jessicali9530/...
Airbnb Amenity Detection Blog post: / amenity-detection-and-...
* Building Computer Vision Datasets in Coco Format*
Computer vision is among the biggest disciplines of machine learning with its vast range of uses and enormous potential. Its purpose is to duplicate the brain's incredible visual abilities. Algorithms for computer vision aren't magical. They require information to perform, and they'll only be as powerful as the information you provide. Based on the project, there are various sources to obtain the appropriate data.
The most famous object detection dataset is the Common Objects in Context dataset (COCO). This is commonly applied to evaluate the efficiency of computer vision algorithms. The COCO dataset is labeled, delivering information for training supervised computer vision systems that can recognize the dataset's typical elements. Of course, these systems are beyond flawless, thus the COCO dataset serves as a baseline for assessing the systems' progress over time as a result of computer vision studies.
In this article, we have discussed Coco File Format a standard for building computer vision datasets, object detection, and image detection methods.
** Coco File Format is a standard for building computer vision datasets **
Analyzing visual environments is a major objective of computer vision; it includes detecting what items are there, localizing them in 2D and 3D, identifying their properties, and describing their relationships. As a result, the dataset could be used to train item recognition and classification methods. COCO is frequently used to test the efficiency of real-time object recognition techniques. Modern neural networking modules can understand the COCO dataset's structure.
Contemporary AI-driven alternatives are not quite skillful of creating complete precision in findings that lead to a fact that the COCO dataset is a substantial reference point for CV to train, test, polish, and refine models for faster scaling of the annotation pipeline.
The COCO standard specifies how your annotations and picture metadata are saved on disc at a substantial stage. Furthermore, the COCO dataset is an addition to transfer learning, in which the material utilized for one model is utilized to start another.
** Tutorial to build Computer vision dataset using Datatorch **
Step0: Discovering Data
Solving any machine learning problem first starts with data. The first question is what problem you want to solve. Then the next question is where can I get this data.
In my case (hypothetical), I want to build an ML model that detects different dog breeds from photos. I am sourcing this relatively simple Stanford Dogs Dataset from Kaggle
Step1: Create New Project
Then go to Dataset tab from the left navigation bar, click on + to create a new dataset named dogtypes. After that you can easily drop the images
Step2: Onboard Data
Then go to Dataset tab from the left navigation bar, click on + to create a ney
w dataset named dogtypes. After that you can easily drop the images
Or there is another option to directly connect to a cloud provider storage (AWS, Google, Azure)
Step3: Start Annotating
If you click on any of the image in the dataset, it will directly lead you to the annotating tool
Step4: Export to Annotated Data to Coco Format After you are done annotating, you can go to exports and export this annotated dataset in COCO format.
#cocodatasets #computervison #datatorch #cocoformat #machinelearning #artificialintelligence #ai #ml
Originally published in anujsyal.com/building-compute... 
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I will try to implement.
Very well explained. Thanks.
Love the content Anuj! Keep it coming!
Thanks a lot!
Amazing!
Excellent & useful information
Thank you!
Awesome! this video is pretty cool. How big of a dataset is allowed in datatorch? I am looking to create a 29-class object detection (just the bounding box with its classification).
Thank you for watching the video. Yes data torch should be able to take this much load easily. The files stored are on cloud, you can also connect to your personal cloud with keys.
Hope this helps.
hi anuj , this was great and really well explained, how do we now get this dataset and load it into pytorch using the dataloader and then split into the data and target ?
Hello Talha, first of all thanks a lot for watching my video.
I have used pytorch based detectron2 to train my object detection models, It comes with pre-built methods to import coco datasets detectron2.readthedocs.io/en/latest/tutorials/datasets.html .
If you are building something simpler just with pytorch, I think you can inherit torch.utils.data.Dataset and define your own getter to read the structure.
Hope this helps.
Excellent tutorial! How many images of a particular class do you need for good detection accuracy?
Hello Karlo, First of all thanks for watching the video.
Then number of images actually varies on the problem that is being solved. If you are training something from scratch you would need a much bigger dataset. If you want to work with smaller datasets, I would recommend using transferred learning on pre-existing models.
Hope this helps
@@AnujSyal Thank you Anuj for your reply! I am new to machine learning. I will soon be writing a final thesis for my bsc. degree. Would it be possible to contact you to ask you a few questions? I can also write here if you prefer that. I see you are very knowledgable in ML and if you would be willing to advise me on a few small things, it would be extremely helpful. Please let me know. Cheers!
Thank you very much for your amazing video I have one question please, regarding the exporting data part is it possible to annotate for example 2 images and I explore them in COCO format and then resume the annotation process for the other images from image 3 or I have to start from scratch?
I think so, whenever you export the dataset, it should be in json format, this can be later on imported easily in datatorch
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
Hey Thank you for watching my video, which tool are you using for annotation?
@@AnujSyal Thanks for reply, but I already got it working I had to use an numpy maximum to avoid overlapping of labels
Please help me. I have tried up to exporting but I don't know how to use it using javascript. Do you have an example for that? New subscriber here. Thank you.
Hey thanks for subscribing! Sorry I would need a bit more details. Are you trying to export annotated dataset? And are you trying to read this json export?
@@AnujSyal Hi, thanks for your quick response. My capstone presentation will be next friday and I'm at the bottleneck because of this. I have the exported dataset (json file) already. But I don't know how to use it. Can you show an example of that in your next video? I'm using javascript only.
@@AnujSyal how about a link to the repo of that project with custom dataset(not coco pretrained) using javascript or reactjs
Hey Dan, for training you can use frameworks such as pytorch or tensorflow. Sharing a blog post that trains on a custom coco dataset format: medium.com/fullstackai/how-to-train-an-object-detector-with-your-own-coco-dataset-in-pytorch-319e7090da5
Also if you want of a more visual tool to do that I think you can consider Perceptilabs, I have covered that in my channel www.perceptilabs.com/
Hope this helps
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