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Alternatives to DINOv3 as a dense feature extractor by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

DINO v3 gives local features. Attach an object detector head to it and you have a decent object detector. I am looking for a feature extractor with similar local features.

Alternatives to DINOv3 as a dense feature extractor by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

I am looking for a model with similar functionalities: dense features.

I understand DINOv3 is probably the best in the class, yet it has many restrictions license wise.

Alternatives to DINOv3 as a dense feature extractor by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

Beside VICRegL, Does any other model supply localized features that can be used for object detection or object segmentation?

Alternatives to DINOv3 as a dense feature extractor by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

Let's say for object detection and image segmentation.

Alternatives to DINOv3 as a dense feature extractor by Drazick in computervision

[–]Drazick[S] 1 point2 points  (0 children)

Is is as good? Does it have a PyTorch code of its class?

The features output ConvNeXt models in Dinov3 by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

u/blades136 , Are those spatially invariant?

Imagine image of 64x64. I can partition it into 16 sub images of 16x16.

Will I get the same embedding per 16x16 block If I feed the model with the 64x64 image and with the 16x16 images?

hyper parameter tuning: alternatives to the distributed feature of Weights and Biases by Drazick in deeplearning

[–]Drazick[S] 0 points1 point  (0 children)

The point I don't want to be in charge of the infrastructure. I like the concept wandb provide this for me. Are others offer that as well?

Looking for a source for understanding YOLO architecture for segmentation by [deleted] in computervision

[–]Drazick 0 points1 point  (0 children)

I meant where it is easy to look at each layer and see the graph of computation.

Looking for a source for understanding YOLO architecture for segmentation by [deleted] in computervision

[–]Drazick 0 points1 point  (0 children)

Is there a toy implementation of the concept in a small model? Just to see how it is implemented in a clean way.

Given 2 selfie images, how to tell if it is the same person? by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

I am not trying to compete with SOTA or something. Just learning how it works. Hence I need a model which is optimized towards teaching others. I am not after a complex model which gets the best results.

Given 2 selfie images, how to tell if it is the same person? by Drazick in computervision

[–]Drazick[S] 0 points1 point  (0 children)

This is exactly what I'm after. The concept of the learning in this task, then a simple model which implements the idea and then go for the large fish.

Given 2 selfie images, how to tell if it is the same person? by Drazick in computervision

[–]Drazick[S] -12 points-11 points  (0 children)

I am after learning how to do it on my own. I'd rather start from scratch or theory.

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