# ResNet Block vs ViT-B/16

Convolution against attention for images.

**ViT-B/16 has 8.4M more parameters than ResNet Block: 8 layers added, 4 removed, 3 changed.**

Source: https://neurarch.com/diff/resnet-block-vs-vit-b16.html

## Sides

| | ResNet Block | ViT-B/16 |
|---|---|---|
| Layers | 7 | 11 |
| Parameters | 74K | 8.4M |
| Input | 64 × 32 × 32 | 3 × 224 × 224 |
| Output | 64 × 32 × 32 | 196 × 1000 |
| Forward-passes | yes | yes |
| Est. train cost | $0.045 | $0.105 |
| T4 16GB | fits | fits |
| A100 40GB | fits | fits |
| H100 80GB | fits | fits |

## Deltas (ViT-B/16 relative to ResNet Block)

- Parameters: +8.4M (114× the size)
- Layers: +4
- Added 8, removed 4, changed 3, unchanged 2

## Layer by layer

| # | Status | ResNet Block | Params | Output | ViT-B/16 | Params | Output |
|---|---|---|---|---|---|---|---|
| 1 | changed (shape) | Input (Input) |  | 64 × 32 × 32 | image (Input) |  | 3 × 224 × 224 |
| 2 | removed | Conv2D_1 (Conv2d) | 640 | 64 × 32 × 32 |  | |  |
| 3 | added |  | |  | patch_embed (Patch Embed) | 591K | 196 × 768 |
| 4 | added |  | |  | pos_embed (Positional Encoding) |  | 196 × 768 |
| 5 | added |  | |  | dropout (Dropout) |  | 196 × 768 |
| 6 | changed (type, normalizedShape) | BatchNorm_1 (Batch Norm) |  | 64 × 32 × 32 | norm_1 (Layer Norm) | 1.5K | 196 × 768 |
| 7 | removed | ReLU_1 (Relu) |  | 64 × 32 × 32 |  | |  |
| 8 | removed | Conv2D_2 (Conv2d) | 640 | 64 × 32 × 32 |  | |  |
| 9 | added |  | |  | attn (Multi Head Attention) | 2.4M | 196 × 768 |
| 10 | added |  | |  | residual_1 (Add) |  | 196 × 768 |
| 11 | changed (type, normalizedShape) | BatchNorm_2 (Batch Norm) |  | 64 × 32 × 32 | norm_2 (Layer Norm) | 1.5K | 196 × 768 |
| 12 | added |  | |  | mlp (Feed Forward) | 4.7M | 196 × 768 |
| 13 | same | Add (Add) |  | 64 × 32 × 32 | residual_2 (Add) |  | 196 × 768 |
| 14 | removed | ReLU_2 (Relu) |  | 64 × 32 × 32 |  | |  |
| 15 | added |  | |  | norm_final (Layer Norm) | 1.5K | 196 × 768 |
| 16 | added |  | |  | head (Linear) |  | 196 × 1000 |
| 17 | same | Output (Output) |  | 64 × 32 × 32 | class_logits (Output) |  | 196 × 1000 |

## What this is not

- The two are priced at different declared inputs (64 × 32 × 32 against 3 × 224 × 224), so memory, cost and GPU fit are each right about their own model and are not a comparison between them. The layer and parameter deltas are unaffected.
- Parameter counts are derived from the graph, not read from a checkpoint. They are exact for a graph that is fully specified and approximate for one that is not.
- Cost and GPU fit are estimates from the graph under one set of assumptions, not measurements of a run.

## Graphs

- ResNet Block: https://neurarch.com/templates/resnet-block/model.json
- ViT-B/16: https://neurarch.com/templates/vit-b16/model.json
- Check a graph of your own: `POST https://www.neurarch.com/api/v1/plan`