3.6 KiB
Other Parameters
model: vgg16 · method: Guided IG · composition: invert · colormap: magma · parameter: varies · all others at default
| Parameter | Default | Effect |
|---|---|---|
| intensity | 0.75 | How strongly the saliency map blends onto the original. 0 = invisible, 1 = full replacement. |
| contrast | 1.5 | Power function applied to saliency values. Above 1.0 makes top regions pop and low regions fade. 2.5–3.0 is extreme — only the very highest attention survives. |
| threshold | 0.25 | Minimum saliency value to count as "attended to." Used mainly by isolation and cutout. Higher = sparser, more selective output. |
| class_index | -1 (auto) | Which ImageNet class to visualize attention for. -1 auto-detects the model's top prediction. Overriding forces the model to answer a different question — e.g. class 207 (golden retriever) on a human portrait shows attention for a question it was never meant to answer. |
| input_size | 224 | Models require fixed-size square inputs. 224×224 is standard; InceptionV3 requires 299. Not adjusted here — most models require 224 to run correctly. |
| use_smoothgrad | true | Adds SmoothGrad noise-averaging on top of any method. Produces smoother, more aesthetically stable results at the cost of speed. |
| smoothgrad_samples | 25 | How many noisy passes to average when SmoothGrad is active. 25 balances smoothness and speed; 50+ is noticeably smoother but proportionally slower. |
Intensity
Default 0.75 — chosen as the point where the saliency map clearly dominates while still leaving enough of the original image readable as context.
0.5 (lower) 
1.0 (full) 
Contrast
Default 1.5 — adds drama without becoming so aggressive that only a handful of pixels survive. Values above 2.0 start to feel like thresholding.
1.0 (flat, no adjustment) 
2.0 (more aggressive) 
Threshold
Default 0.25 — eliminates low-confidence gradient noise while preserving the meaningful attended region. At 0.0 everything is included; at 0.5 only the top quarter of saliency values show.
0.0 (nothing filtered) 
0.5 (aggressive filtering) 
Class Index
Default -1 (auto) — visualizes attention for whatever the model actually predicted. Overriding this forces the model to attend to features of a class it wasn't trying to find, producing maps that can feel alien or misaligned — attention patterns searching for something that isn't there.
class 1 (slight displacement) 
class 10 (larger displacement) 
Input Size
Default 224 — not demonstrated here because most models require this exact size to run correctly. InceptionV3 is the only exception, requiring 299.
Use SmoothGrad
Default true — the smoothing improvement is significant and 25 samples is fast enough that the tradeoff is worth it for almost all use cases.
false (raw gradients, no smoothing) 
Smoothgrad Samples
Default 25 — a reliable balance. Below 10 starts to look patchy; above 50 the improvement becomes marginal relative to the time cost.
50 samples 