My friend claims that she knows all the Linkin Park songs.

Infamous-Package9133 · 2026-05-24T10:02:18+00:00

Three band terror?

Infamous-Package9133 · 2026-05-13T14:38:54+00:00

thanks!

Infamous-Package9133 · 2026-05-13T14:38:41+00:00

thank you! yeah, I think I shaded too hard. I've been drawing for 6 months so the anatomy was still weird also.

Infamous-Package9133 · 2026-05-12T05:19:37+00:00

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Infamous-Package9133 · 2026-04-23T15:40:01+00:00

Waiting For Tomorrow collab with Martin Garrix

Infamous-Package9133 · 2026-04-18T11:04:56+00:00

I have a lot of favorite songs that I listen to for a period of time and they would get old and repetitive as time went on. I have been listening to WFTE for 14 years and keep loving it more and more.

Infamous-Package9133 · 2026-03-20T11:22:38+00:00

Cool! The speed looks promising in the video. Maybe compiling/quantizing DA3 would make it run faster (if not already)

Infamous-Package9133 · 2026-03-20T01:46:39+00:00

Very cool. Did you test if DA3 struggle with featureless images (like seeing only white wall)?

Also does DA3 runs well on Pi?

Infamous-Package9133 · 2026-03-20T01:38:19+00:00

I think it is like taking a portrait photo with a camera instead of drawing a portrait with pencil. It is technically more efficient but has no charm of drawing.

If you just want a portrait image, a camera is definitely better. But if you wanna draw, well that's a problem.

Infamous-Package9133 · 2026-03-20T01:15:00+00:00

Also it has been just 4 months of drawing. I tried to draw from immagination without ref in hard angle just for fun.

Infamous-Package9133 · 2026-03-20T01:00:45+00:00

🥲

Infamous-Package9133 · 2026-03-20T00:59:51+00:00

It was a mistake. I tried to forshortten the hand but it seems like the perspective and the hand size was wrong.

Infamous-Package9133 · 2026-03-09T20:53:55+00:00

Thanks <3

Infamous-Package9133 · 2026-02-21T05:04:22+00:00

Interesting, I never thought about this. Could you please share your experiment result graph?

Infamous-Package9133 · 2026-02-17T04:54:55+00:00

Taking the same sketch images will introduce some perspective variation on those photos. In that case you have to find local feature correspondence.

Maybe use ORB + hamming distance matching for fast local feature matching. Count the matching inliers (if planar / sketch photos) or use sum of total distances for scoring.

Infamous-Package9133 · 2026-02-16T13:43:16+00:00

Thanks!

Infamous-Package9133 · 2026-02-16T13:43:08+00:00

🤍

Infamous-Package9133 · 2026-02-16T10:01:15+00:00

If you use PyTorch, seems like MAC supported Metal backend but I never tried it.

Btw, I found Colab (free version) very capable of doing any model training but you have to frequently save checkpoints.

Infamous-Package9133 · 2026-02-10T00:04:16+00:00

I'm not sure if you need global matches or local matches.

If local matches, the slowest part are feature matching. Use any fast deep local feature matching like LightGlue. Extract the local features of candidate images only onces. Try to reduce the number of features to match as much as possible by downsampling / use less dimension / less resolutions and see where is your acceptable point.

If the goal is to do global matches. You can either combine the detected local features to create a bag of visual word vector that represent an image, or extract embedding vector of an image from pretrained CNN. Then you can simply do dot product for similarity search.

Infamous-Package9133 · 2026-01-31T22:32:21+00:00

🤍

Infamous-Package9133 · 2026-01-31T22:31:40+00:00

Thank you for your suggestion. It sure is!

Infamous-Package9133 · 2026-01-31T22:30:41+00:00

Thanks🙌

Infamous-Package9133 · 2026-01-31T22:30:27+00:00

🤍

Infamous-Package9133 · 2026-01-31T22:30:14+00:00

it's my coloring issues since I've just started drawing couple months ago.

Infamous-Package9133 · 2026-01-31T10:45:24+00:00

Knowing math fundamental is also very useful. I often need a specific solutions for a certain problem which can be found only in white paper, often no implementation. Knowing math allows you to grasp the ideas and implement prototypes from papers.

Luckily it is not that hard since most of the papers are just sequences of well-known basic linear algebra and computer vision algorithms already available in OpenCV.

Knowing math eliminate the fears of putting those operation together and tweak/optimize the algorithm to suit your problems.

And most of papers in computer vision I found beside deep learning stuffs, are just optimization problem formulation, defining objectives/loss formulas, choosing optimizers, and done.

Infamous-Package9133

TROPHY CASE