quiz bu lëkkale ak tegtal · Diggante Niveau
Quiz buy soppi gis-gis
Saytu jetons patch ak bàyyi xel ci ViT, ak li gëstukat yi gis, leeral ci position, lim patch, ak tànneefi stade classification.
Laaj 1 de 8
How does the original plain ViT convert an image into input tokens for its encoder?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- 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
- Which mechanism supports direct interactions between allowed patch tokens in the original dense ViT encoder?
- Fexe sa bopp
- Only a fixed local max-pooling operation
- Only alphabetical ordering of patch labels
- Only a random image crop
- 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
- Lan mooy njariñu enkodaasu position ci Transformatëru gis-gis?
- Ngir wàññi dayo fichier nataal bi
- ngir jaxase patch yi ci anam wu mucc ngir yokk
- Ngir wax model bi fi patch bu nekk nekkoon ci nataalu njëkk bi
- Ngir soppi nataali melo yi ci grayscale
- Benn ci njariñu bàyyi xel ci sa bopp ci patch nataal yu bari mooy njëg bi dafay yokk:
- Ci anamu kaare ak limu patch yi
- Lineer ak limu patch yi
- Du dara ci dayo nataal bi
- Ak limu chaine yu am melo yi kese
- 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
- 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
- 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