Видео

Excellent presentation! Thank you.

Hello sir, How are you? I am a regular viewer of yours. I follow your RUclips channel. I really your videos. But your videos get very few views. Have you thought about this?

Super c'est modèles sont disponibles pour l'ensemble de la planète ? l'Afrique par exemple

Amazing work!

WOW. This looks so amazing. I can't wait to use this!

Really interesting

Thank you for sharing the video. Does this plugin assume a vector engine like qdrant is used as backend?

Thank you for sharing this video on the Active Learning plugin. Is it possible to use the plugin for multi-class multi-label tasks as well?

When I try the dev install process in a git bash terminal, it fails at a point because of a package error. "Collecting shapely>=1.7.1 (from -r requirements\extras.txt (line 7)) Using cached shapely-2.0.6-cp312-cp312-win_amd64.whl.metadata (7.2 kB) ERROR: Could not find a version that satisfies the requirement open3d>=0.16.0 (from versions: none) ERROR: No matching distribution found for open3d>=0.16.0" How can this be solved?

Very interesting demo; would you mind sharing the Colab link?

ok, and how does one start it?

This was very helpful! Llama Index grows so fast, it feels overwhelming for a beginner.

!second comment

I want to work with my custom dataset. I'd like you to show me how to do it and which benefits I can get using your product. Examples, how can I refine my own data with fiftyone

Isn't the "Grid Trick" similar to using ControlNet, a type of model for controlling image diffusion models by conditioning the model with an additional input image?

Love your product!

How to we execute the plugin logic in the code? This doesn't seem to work: logging.info("removing approximate duplicates") operator_uri = "@jacobmarks/image_deduplication/remove_all_approximate_duplicates" params = { "sim_choices": "sim", # You may need to adjust this based on your similarity run key "threshold_value": 0.4 } # Create an invocation request request = foe.InvocationRequest(operator_uri, params=params) # Create an executor and execute the request executor = foe.Executor(requests=[request]) result = executor.trigger(operator_uri, params=params) print(result.to_json()) # logging.info(f"Found approximate duplicates: {result.result}") return result

The video was great, thanks mate for explination.

Wonderful 👍

Great!

the search result are only online images? or can it be local images?

Made that look *way* too easy. I spent a whole hour last night trying to get the first line of code to work! It was because my Python paths were thrown about the place

How to build the js part of code to generate umd.js file in dist folder. I am build using yarn build but the generated umd file is not working and not opening new panel. Please help

How to build the js part of code to generate umd.js file in dist folder. I am build using yarn build but the generated umd file is not working and not opening new panel. Please help

great tutorial, can you use a local instance of SD?

It is great that you give us a list of next steps, but a link to each of these points would have been nice!

nice job！

This is good! But i believe the data should also grab eye movement. Eye movement is crucial to map intention and will aid in robot navigation. Apple's headset has the hardware to monitor both eye direction and head direction.

I have a idea for build a autonomous drone using computer vision to detect objects that is labled with a GPS location before.

please slide share?

How does it select which images would be kept as "representatives" and which removed?

I want words like these intitle:"keyword" For better search efficiency for topics

Is it possible to use this and find the most similar image given user submitted photos? For example I'm trying to do something to detect trading cards, where the input would be photos of cards submitted by users.

Hello, I downloaded and installed FiftyOne, but I don’t know how to use it. All your videos didn’t explain how to use it.

I love this

good

Thanks for clear explanation❤

Thank you so much bro. Nice tutorial.

getting Not Found

Thanks, great overview

Look promising, I was going through your tutorial, and I was hoping to see how you can import your own database.

🤩 Promo'SM

Can you perform the initial labeling on images that have not been annotated yet? On part 5 and I have not seen that information yet. Did I miss it?

Can you edit/correct or add/remove annotations directly in FiftyOne?

I am really excited about this product! Thank you for this hands-on video!

"Wow, this video is incredibly informative and well-produced! The speaker does a fantastic job of explaining the complex topic of speech recognition and the new Whisper model from OpenAI in a way that's easy to understand. Great job, highly recommended to anyone interested in this field!"

splendid 🙂✌️️️!! Find out how your competition ranks better = 'Promosm'!!

how to add our own dataset into FiftyOne. I want to label my own data.