hello, i need some help.

yup bro @@naveedarif3565

but I was wondering on how the information flow works. Like an Interpretation why it is able to see shapes. I did some research and made this summary of my findings: kzbin.info/www/bejne/mpqYmGeEl5mbgdU

Thank you for the kind words. I have plans to make segmentation videos. ✌️

Thank you. I’ll try to make one or share some resources in a few days.

@@LightsCameraVision That'll be great :)

Hello, Thank you. Yes, there are image and class repetitions in training tasks. But the meta training classes/images are different than the meta testing classes/images. Btw the Omniglot data has 1623 alphabets from 50 different languages, and each alphabet has 20 images. So total number of images is 32460. Could you please explain a little more what do you mean by you have good images and bad images?

@@LightsCameraVision I have 2 kinds of vehicle parts each part has 2 types good part and faulty part, the problem is that for faulty part I have just 5 images and good part are around 700. Therefore, I am looking for some solutions which require less training data. Do you think few shot will work in this case?

5 is very low. Of course, the best solution is to get more images if possible. Otherwise, you can look for data that is similar to what you are trying to do then do the meta training on that, and then meta test on your data. You can also look at zero-shot learning.

Noted. Thanks for the feedback. Didn’t have access to my regular mic, probably that’s the reason. Hope it’s not that bad. ✌️

@@LightsCameraVision accent is good enough bro, keep it up. But having a better mic is a nice boost to have.

audio is ok though

@@LightsCameraVisionaccent ok, no issue

Appreciate it. ✌️

Thank you. I’m sharing two links here, the first one is for PyTorch dataloader for the few-shot learning and the second one is for TensorFlow. You may have to modify it for your case. Hope it helps. ✌️ github.com/sicara/easy-few-shot-learning/blob/master/easyfsl/samplers/task_sampler.py github.com/schatty/matching-networks-tf/blob/master/matchnet/data/mini_imagenet.py

Yes, you can.But I'm skeptical about the performance of ML algorithms using meta-learning on complex NLP tasks. The recent papers are written on neural networks or transformers. But you can definitely try.

At a high level, the goal of both triplet loss is same as metric learning. We use them to learn a representation function. But they are doing it in different ways. Triplet loss is usually used in self-supervised learning where it learns by comparing represented vectors. Whereas metric learning algorithms learn a function that maps instances to a new space and later on a test instance is projected on that space and classified based on the closest distance to a learned class. Other types of meta-learning algorithms are different than construction learning (triplet loss).

Appreciate the kind words. ✌️ Yes, you can. Meta learning algorithms are model agnostic in general. There are some work on this. Check these out. arxiv.org/pdf/2203.01482.pdf edoc.ub.uni-muenchen.de/24557/1/Probst_Philipp.pdf ieeetv.ieee.org/video/meta-algorithms-in-machine-learning

Appreciate the kind words. Thank you. ✌️

Thanks for your kind words. It's great to see that you are also passionate about point cloud processing. Thanks for the suggestions, I'll keep them in mind. ✌️

Thank you. Yes, you can use it for your case. I have only seen people using FSL in computer vision and NLP. But I don't see why you can't use it in other domains, people may already have. ✌️

I would suggest you to fix your conda not found error first and then install the packages/dependencies. You can run "conda --version" to check if it's installed properly.

Install it 3 to 4 times

And check to still got same error

So this may mean many things. You may not have anaconda installed or maybe conda isn't added in the path variable. If you don't have anaconda installed go to www.anaconda.com/ and follow the instructions to download it. If you already have it downloaded you have to follow something like this monovm.com/blog/conda-command-not-found-fixed/ or other StackOverflow posts that fits your case. Then follow the video to create the environment and install the necessary dependencies. ✌️

I installed conda and after installing mayaavi package I was still getting other error called as opengl error

Appreciate the kind words. Thank you. ✌️

Thank you. I'm glad you found it useful. ✌️

I'm glad that you found it helpful. Here is a small subset of the KITTI dataset that you can download. pl-flash-data.s3.amazonaws.com/KITTI_tiny.zip ✌️

@@LightsCameraVision Thanks so much for the dataset! Is it also possible to get the images for the subset you gave me? The script doesn't run without the images corresponding with the labels, calibration, and pointcloud data. If not, is there another way to run the script without the images?

Hi Daniel, try this subset of KITTI data. drive.google.com/drive/folders/1Y6QKWAEN0mUuz2lq7D1pJNCsg2cnwhjC?usp=sharing

I'm glad that you found it helpful. Thanks for pointing out the error. I have fixed it. Check out the updated Colab link in the video description. ✌️

​@@LightsCameraVision Thank you!!

Appreciate the kind words. 🙂✌️

If you mean how to generate point cloud data, then you need a LiDAR device to capture point clouds, or you can use a simulator to augment real point cloud with synthetic obstacles and environment. There are simulators like CARLA you can check out. For data annotation, you can try this tool (supervise.ly/lidar-3d-cloud/). It supports the KITTI format. Since KITTI data/format is used as a benchmark in many algorithms, generating annotations for custom data in KITTI format is a good idea. This tool is well documented. You can also read this Medium post (medium.com/deep-systems/releasing-first-online-3d-point-cloud-labeling-tool-in-supervisely-4faca42b5d6e) about this tool. If you are looking for models for segmentation then you may look into PointNet, PointSIFT, Squeezesegv3 and there are many more models. Hope it helps. ✌️

Appreciate it. You definitely can do it. You just need to tweak a little at the end. I remember this paper using PointNet for regression. Check it out. arxiv.org/pdf/2010.04865.pdf ✌️

@@LightsCameraVision Thanks so much for taking the time to point me in the right direction! Will check it out

Happy to help.

Appreciate it. You can use it. You may need to change the code a little bit. Please check this repo github.com/kargarisaac/PointNet-SemSeg-VKITTI3D They used PointNet for semantic segmentation on Virtual KITTI dataset. It should give you some idea. ✌️

I’m glad it helped you. Thanks for the kind words. ✌️

Hello, I'm a little busy right now with some deadlines. I'll look into the issue soon. Please comment the error you are getting when you tried it with Fashion MNIST. Thanks!

Não é mais o modelo mais preciso; existem modelos mais novos com melhor precisão. Com dados suficientes, ele deve ser capaz de lidar com uma quantidade significativa de classes. No entanto, não funciona bem em cenas complexas. Mas este é um dos primeiros modelos que deram início a tudo. Alguns dos novos modelos publicados recentemente ainda usam PointNet como backbone para extração de recursos ou outros motivos. Por ser tão fácil de usar, tenho certeza de que muitas pessoas na indústria começam com ele e, em seguida, desenvolvem de acordo com suas necessidades.

It should work assuming you have enough computational resources. The original PointNet architecture takes 1024 points as input which is definitely not much. You can also modify this for your project so that it can handle more points. Depending on what you are trying to classify you may not need many points or a big point cloud for each object. You can always downsample points.

Thank you for the kind words. I really appreciate it. 🙂 Thanks for watching. ✌️

Appreciate the kind words. Thanks for watching. ✌️

Are you getting any error?

@@LightsCameraVision No error sir

​@@yasminbanu2597 I'm assuming you are using the same GitHub repository for KITTI data. Then make sure you have installed all the necessary libraries and are using the correct command (like below) in the terminal/shell. python kitti_object.py --show_lidar_with_depth --vis

I’m very glad that you found this video helpful. 🙂 Thanks for watching and for the suggestion. A point cloud segmentation video is in the making. ✌️

Thank you for your reply

Appreciate the kind words. 🙂✌️