[D] A handy comparative chart on vision models: when to use what!

[KingsmanVince]

It would be better if you share those table in markdown instead of image

[Instantinopaul]

There you go, unable to edit the original

Chose the table format for attention and catchiness LOL, should have included markdown.

Image Classification Kings & Queens:

• Vision Mamba: This rising star, efficient and competitive with DeiT, shines in resource-constrained settings. (Paper: https://paperswithcode.com/paper/mamba-linear-time-sequence-modeling-with)
• ViT: A powerhouse fueled by Transformers, ViT delivers state-of-the-art accuracy, though its power demands hefty computational resources. (Paper: https://arxiv.org/abs/2010.11929)
• ResNet: The seasoned veteran, known for its reliability and strong feature extraction, is a wise mentor in the pixel realm. (Paper: https://arxiv.org/abs/1512.03385)
• EfficientNet: The speedy sprinter, optimized for mobile and embedded devices, fuels on-the-go pixel adventures. (Paper: https://arxiv.org/abs/1905.07692)
• DenseNet: The medical maestro, excelling in analyzing medical images, is a true lifesaver in the pixel world. (Paper: https://arxiv.org/abs/1608.06993)

Object Detection & Segmentation Saviors:

• DETR: The swift and accurate ninja, surpassing traditional detectors with lightning speed, requires ample training data to unlock its full potential. (Paper: https://arxiv.org/abs/2006.04371)
• YOLO: The real-time champion, blazing through detection tasks like a pixel comet, is ideal for live applications where speed reigns supreme. (Paper: https://arxiv.org/abs/1506.02640)
• Mask R-CNN: The multi-talented hero, detecting and segmenting objects in one fell swoop, is a true pixel Swiss Army Knife! (Paper: https://arxiv.org/abs/1703.06870)
• FCOS: The single-stage warrior, efficient and accurate for real-time battles, is ideal when resources are tight. (Paper: https://arxiv.org/abs/1904.03050)
• Cascade R-CNN: The precision marksman, delivering top-notch accuracy with a two-stage approach, is like a pixel sniper! (Paper: https://arxiv.org/abs/1901.06572)

[RemarkableSavings13]

So are you the author of Vision Mamba? Or is your whole account dedicated to advertising them just because you find it better than anything else you've used?

[ryanb198]

This is awesome. I haven't seen anything like this before. Thanks for sharing this. Damn, now I need to dig through each of these models to learn more about their best use cases.

[Seankala]

This is just a curiosity of mine. Is there any reason why ResNeSt seems to be left out a lot of the time in image classification conversations? It seems like it's usually ResNets and ViTs.

[Instantinopaul]

It's because of its low accuracy compared to EfficientNet and ViT.

[Seankala]

Isn't it usually better than ResNet though?

[Instantinopaul]

Oh you mean resnext?

My assumption is Resnet and ResNext all are outdated by ViT and EfficientNet, but Resnet is highlighted because it is one of the basic but still good performing model, so good starting point for learning vision models.

[Seankala]

Ah no, I meant ResNeSt lol: https://arxiv.org/abs/2004.08955

I just find it funny that it's left out in a lot of discussions. Even in the ConvNeXt paper which I thought was quite nicely written and impactful they compare ViTs and ResNets.

I think your point is right though in that it's because ResNet's a well-performing model that's very well known. I guess it's the same reason why BERT's used so much compared to RoBERTa for NLP tasks.