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    Home»Machine Learning»Vision Transformers: Game-Changer or Just Pixelated Hype? | by Mehmet Özel | Technology Core | Jun, 2025
    Machine Learning

    Vision Transformers: Game-Changer or Just Pixelated Hype? | by Mehmet Özel | Technology Core | Jun, 2025

    Team_AIBS NewsBy Team_AIBS NewsJune 27, 2025No Comments1 Min Read
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    The pc-vision rumor mill has topped Imaginative and prescient Transformers (ViTs) “the following CNN-killer.”
    However are they actually poised to upend picture processing — or did the advertising group merely sprinkle consideration on our eyeballs? I educated a tiny ViT and a plain-vanilla CNN facet by facet on CIFAR-10 and saved notes.

    • Pre-2012 SIFT, HOG, LBP: engineers hand-designed filters for edges & corners.
    • 2012–2020 CNNs: layers discovered options mechanically; ImageNet glory adopted.
    • 2020+ ViTs slice a picture into patches → deal with each patch like a phrase token → run full-blown self-attention. Immediately every pixel “talks” to each different pixel.

    That’s the elevator pitch — now let’s peek below the hood.

    “Generated by ChatGPT-o3”
    # Extract 4×4 patches from 32×32 photos (CIFAR-10)
    patches = tf.picture.extract_patches(
    photos, sizes=[1, 4, 4, 1],
    strides=[1, 4, 4, 1], charges=[1, 1, 1, 1],
    padding='VALID')

    # Flatten every patch, then challenge to the mannequin dimension
    patch_dims = patches.form[-1]
    patches = tf.reshape(patches, [-1, num_patches…



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