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.
Ibeere 1 ti 8
How does the original plain ViT convert an image into input tokens for its encoder?
Awotẹlẹ gbogbo ibeere8 ibeere
Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.
- 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?
- Ifarabalẹ ara ẹni
- 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
- 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
- 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
- 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