Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Iran Transformers adanwo

Explore ViT’s patch tokens and attention, with original research findings, positional information, patch counts, and classification-state choices.

Jẹmọ itọsọna awọn ọnaIran Ayirapada
Ibeere 1 ti 8

How does the original plain ViT convert an image into input tokens for its encoder?

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Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. How does the original plain ViT convert an image into input tokens for its encoder?
    • It must read one raw pixel per recurrent step
    • It linearly embeds flattened non-overlapping patches
    • It converts the whole image into a mandatory single scalar
    • It removes all learned input projections
  2. Which mechanism supports direct interactions between allowed patch tokens in the original dense ViT encoder?
    • Ifarabalẹ ara ẹni
    • Only a fixed local max-pooling operation
    • Only alphabetical ordering of patch labels
    • Only a random image crop
  3. Which architectural difference did the original ViT study discuss relative to convolutional networks?
    • A guarantee that it contains fewer parameters than every CNN
    • An inability to process color images
    • A guarantee that every vision task needs the same huge dataset
    • Fewer convolution-style locality and translation-equivariance biases
  4. Kini idi ti awọn fifi koodu ipo ni Amunawa Iran kan?
    • Lati compress iwọn faili aworan
    • Lati dapọ awọn abulẹ laileto fun afikun
    • Lati sọ fun awoṣe nibiti alemo kọọkan wa ninu aworan atilẹba
    • Lati yi awọn aworan awọ pada si iwọn grẹy
  5. Idaduro ilowo ti akiyesi ara ẹni lori ọpọlọpọ awọn abulẹ aworan ni pe idiyele rẹ dagba:
    • Quadratically pẹlu awọn nọmba ti awọn abulẹ
    • Laini pẹlu nọmba awọn abulẹ
    • Ko si rara pẹlu iwọn aworan
    • Nikan pẹlu nọmba awọn ikanni awọ
  6. What conclusion is supported by the original ViT classification experiments under their stated pretraining and transfer conditions?
    • Every transformer must outperform every CNN on every dataset
    • A pure patch transformer could perform competitively with compared CNNs
    • All convolutional operations are invalid for image recognition
    • No training data is needed for image classification
  7. For a 224-by-224 image divided into non-overlapping 16-by-16 patches, how many patch tokens are there before adding a class token?
    • 197
    • 224
    • 196
    • 256
  8. In the original ViT classification design, which encoder state supplies the classification head?
    • Only an unembedded raw input pixel
    • A required random token not processed by the encoder
    • Only the first patch’s original filename
    • The final state of the learned class token