Відео

this guy is so wholesome

Thank you so much...fantastic

Finally I Did it 😭😭😭😭 Guys, Idk what this code means, but you can use this code, I literally put my 4-5 hea in figuring it out with ChatGPT and all, but this finally worked for me 😭😭 Here is the Code, # -*- coding: utf-8 -*- """ Created on Mon Aug 19 20:04:49 2024 @author: User """ import numpy as np import imageio # Reading an image image = imageio.imread('Images/harshface.png') # Print the image array print(image) # Check if the image has an alpha channel if image.shape[2] == 4: # Remove the alpha channel image_rgb = image[:, :, :3] else: image_rgb = image # Apply a blue tint (ensure image is in float type for manipulation) image_tinted = image_rgb.astype(np.float32) * np.array([0., 0., 1.]) # Clip values to ensure they are in the valid range [0, 255] image_tinted = np.clip(image_tinted, 0, 255).astype(np.uint8) # Save the tinted image imageio.imsave('Images/harshface2_tinted.png', image_tinted) # harshface is the image I used haha

Where do you define the eos_token and bos_token in the training text formatting? Also, what is the format of the dataset txt files?

好好好

Hello, tahak you so much for this video it actually helped me a lot. But I still have some questions about few things i still don't understand. Would that be possible to discuss it with you on twitter or something ? I just followed you there but i can't send you a message. If it's not possible for you no problem, thank you for this qualitive content anyway keep this way! Have a great day !

Sir, I have 120 classes will this work in my case ?

Thank you so much for your generosity sharing this knowledge and taking the time to create these well explained videos. I am currently doing a masters in AI and these videos are a great addition, some are even better, or at least easier for me to understand, than my course explanations.

These videos are wonderful. I would like to add that there is a 4th paradigm which is: semi-supervised learning. :)

Which software did you used?

Hey! Your Videos are Amazing. Can you post some Videos On Diffusion.

Perfect

the validation data folder does not have segmentations ! , just 4 images

great video, Now we are waiting for SAM2 using custom data

Hello, I have set of images with size of about 1024x1024 but the size of object to be semantic segmented are big almost like 600x600. My dataset size is too low but not sure if patchify makes sense here?

please what size of ram hae you for training this model ?

I want to apply srgan method to my custom dataset in colab. I tried using your method but ram crashed everytime and I am not able to get a workable model. Can you please help regarding this matter please?

Can i leave some of your videos especially biomedical application..while following whole playlist ?any suggestions

Hey digiisreeniiii❤...do u think being a freshman in a collg i can study this whole playlist one by one want to learn ML 😊plz reply if u see it thnaks

Save Images with annotations is now a paid feature in CVAT. Thus it is not totally free.

Heloo srniiii am just a fresher with combinatorics and calculus knowldege can i understand whole of the playlist ....or is is only for microscopist ?

Nice :)

Thanks!

Very nice

thanks a lot man! great video

Can you work on detecting Parkinson disease using spirals drawing? it's my first project, and I have a dataset of 260 images only. I am not sure if I do data augmnetation on diseased person only or on healthy to

hi there love the content can you please do a video for panoptic segmentation using are own dataset and please make it from scratch meaning how we get the instance segmentation mask and the semantic segmentation mask then how we combine them and run it on own own dataset.

permission to learn sir. thank you

Amazing explanation! Thank you.

great video.

Any body intrested in my project.. im working on a density map estimatatin

Thanks!

After I use this Python code, the result of 0 hour is :The binary True value is 53829. the result of 2 hour of the same sample :The binary True value is 1363498. That makes no sense. I thinking the problem is because of sample 0 hour. The color of scratch area of 0 hour should be more green. I am just learn by myself and who can help me? or leave the E-mail address. Because with pictures, I can explain my question more clear. Thank you so much in advance.

Best tutorial on ARIMA forcasting. Thank you.

I have a question: Can we use label studio, for instance segmentation labeling?

sreeni can you give us the link for the q and a document ? it's not there in your github

Thank you sir!

Thank you for this! Very helpful when you go over basics once in a while

Hi Sree, I am running into a problem with the code which gives me a an error when a load the dataset. Could you please help me with that?--- ValueError Traceback (most recent call last) <ipython-input-20-ee9fee6be012> in <cell line: 1>() ----> 1 example = train_dataset[0] 2 for k,v in example.items(): 3 print(k,v.shape) 6 frames /usr/local/lib/python3.10/dist-packages/transformers/image_utils.py in infer_channel_dimension_format(image, num_channels) 241 first_dim, last_dim = 1, 3 242 else: --> 243 raise ValueError(f"Unsupported number of image dimensions: {image.ndim}") 244 245 if image.shape[first_dim] in num_channels and image.shape[last_dim] in num_channels: ValueError: Unsupported number of image dimensions: 2

What dataset are you using please

Nice tutorial, but wouldn't using any resampling techniques before splitting the data cause data leakage therefor biased performance measurement on evaluation data ?

Hey hi, lov your work .could plz explain how we could annotate SAR sentinel 1 images

Thankyou sir !! It was quite helpful

All fun and games until something made appears behind you

I usually do not comment on videos but had to for this one. Wonderfully explained with step-by-step instructions. Thank you!

Thanks for the video! I am getting the error "ValueError: Unsupported number of image dimensions: 2" in the SAMDataset, and I am strugling to fix it. Anyone with similar error?

For those who aren't patient enough to watch the whole 38-minute video, you can jump to the last 10 minutes where I tweaked my original prompt to use deep learning for segmentation and ran the experiment again.

Thank you very much.

prety useful, thanks!

37:14 is the conclusion. Ur welcome