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Update image paths

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Claire Vlases
2026-06-11 18:04:00 -04:00
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commit b1c63c6cb9
1 changed files with 43 additions and 43 deletions
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README.md
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@@ -75,7 +75,7 @@ VGG models have the largest, most spatially coarse attention patterns of any mod
**VGG19**
![vgg19](model_demo/vgg19.png)
![vgg19](./documentation/model_demo/vgg19.png)
---
@@ -91,11 +91,11 @@ ResNets produce more distributed, semantically coherent attention than VGG. The
**ResNet50**
![resnet50](model_demo/resnet50.png)
![resnet50](./documentation/model_demo/resnet50.png)
**ResNet101**
![resnet101](model_demo/resnet101.png)
![resnet101](./documentation/model_demo/resnet101.png)
---
@@ -112,7 +112,7 @@ DenseNet produces the most distributed, multi-scale attention patterns. Because
**DenseNet121**
![densenet121](model_demo/densenet121.png)
![densenet121](./documentation/model_demo/densenet121.png)
---
@@ -128,7 +128,7 @@ Because features are detected at multiple scales simultaneously throughout the n
**InceptionV3**
![inception_v3](model_demo/inception_v3.png)
![inception_v3](./documentation/model_demo/inception_v3.png)
---
@@ -143,7 +143,7 @@ MobileNet produces the sparsest, most minimal attention maps. It often fixates o
**MobileNetV3**
![mobilenet_v3_large](model_demo/mobilenet_v3_large.png)
![mobilenet_v3_large](./documentation/model_demo/mobilenet_v3_large.png)
---
@@ -165,7 +165,7 @@ These are the different mathematical techniques used to ask "what pixels mattere
The simplest approach: take the partial derivative of the classification output with respect to each input pixel. The result is noisy, almost like static or grain, because it shows every pixel that had *any* influence at all.
![Vanilla Gradients](method_demo/vanilla_grad.png)
![Vanilla Gradients](./documentation/method_demo/vanilla_grad.png)
---
@@ -175,7 +175,7 @@ Takes Vanilla Gradients but runs it many times with small random noise added to
The `smoothgrad_samples` parameter controls how many noisy runs are averaged. More = smoother but proportionally slower.
![SmoothGrad](method_demo/smooth_grad.png)
![SmoothGrad](./documentation/method_demo/smooth_grad.png)
---
@@ -185,7 +185,7 @@ Instead of computing gradients at only the input image, this traces a path from
The `ig_steps` parameter controls how many interpolation steps are taken. More steps = more accurate attribution, slower computation.
![Integrated Gradients](method_demo/integrated_grad.png)
![Integrated Gradients](./documentation/method_demo/integrated_grad.png)
---
@@ -193,7 +193,7 @@ The `ig_steps` parameter controls how many interpolation steps are taken. More s
A variant of Integrated Gradients where instead of interpolating from a black image to the input, it interpolates from a *heavily blurred version* of the image to the sharp version. The difference in what's being integrated changes what gets attributed.
![Blur IG](method_demo/blur_ig.png)
![Blur IG](./documentation/method_demo/blur_ig.png)
---
@@ -201,7 +201,7 @@ A variant of Integrated Gradients where instead of interpolating from a black im
Another IG variant that uses gradient information to choose a smarter integration path, concentrating steps where the gradients are largest rather than taking uniform steps. This produces sharper, higher-contrast attribution maps with more defined edges.
![Guided IG](method_demo/guided_ig.png)
![Guided IG](./documentation/method_demo/guided_ig.png)
---
@@ -209,7 +209,7 @@ Another IG variant that uses gradient information to choose a smarter integratio
A completely different approach. Instead of pixel-level gradient computation, XRAI segments the image into regions using an oversegmentation algorithm, then attributes importance to each region as a whole by measuring how much removing it changes the model's output.
![XRAI](method_demo/XRAI.png)
![XRAI](./documentation/method_demo/XRAI.png)
---
@@ -225,7 +225,7 @@ These control how the saliency map is composited back onto the original image. T
Salient regions appear in full color using the chosen colormap; everything else converts to greyscale.
![isolation](composition_demo/isolation.png)
![isolation](./documentation/composition_demo/isolation.png)
---
@@ -234,7 +234,7 @@ Salient regions appear in full color using the chosen colormap; everything else
Classic heatmap blend. The colormap is alpha-composited over the original at the `intensity` level.
![overlay](composition_demo/overlay.png)
![overlay](./documentation/composition_demo/overlay.png)
---
@@ -242,7 +242,7 @@ Classic heatmap blend. The colormap is alpha-composited over the original at the
Salient regions are bright; everything else darkens.
![spotlight](composition_demo/spotlight.png)
![spotlight](./documentation/composition_demo/spotlight.png)
---
@@ -250,7 +250,7 @@ Salient regions are bright; everything else darkens.
The original image fades to 25% opacity and the saliency map layers over it.
![ghost](composition_demo/ghost.png)
![ghost](./documentation/composition_demo/ghost.png)
---
@@ -258,7 +258,7 @@ The original image fades to 25% opacity and the saliency map layers over it.
Shows what the algorithm *ignores* instead of what it sees. The saliency map is inverted before compositing.
![invert](composition_demo/invert.png)
![invert](./documentation/composition_demo/invert.png)
---
@@ -266,7 +266,7 @@ Shows what the algorithm *ignores* instead of what it sees. The saliency map is
Hard binary mask. Pixels above the `threshold` value show the original image in full; everything below shows a dim version of the heatmap.
![cutout](composition_demo/cutout.png)
![cutout](./documentation/composition_demo/cutout.png)
---
@@ -274,7 +274,7 @@ Hard binary mask. Pixels above the `threshold` value show the original image in
Photoshop-style multiply blend.
![multiply](composition_demo/multiply.png)
![multiply](./documentation/composition_demo/multiply.png)
---
@@ -282,7 +282,7 @@ Photoshop-style multiply blend.
Photoshop-style screen blend. Attention brightens salient regions.
![screen](composition_demo/screen.png)
![screen](./documentation/composition_demo/screen.png)
---
@@ -290,7 +290,7 @@ Photoshop-style screen blend. Attention brightens salient regions.
Outputs three panels side by side: original image | saliency map alone | composite. Shows the full chain of the visualization in one frame.
![triptych](composition_demo/triptych.png)
![triptych](./documentation/composition_demo/triptych.png)
---
@@ -298,7 +298,7 @@ Outputs three panels side by side: original image | saliency map alone | composi
Just the colored saliency map with no original image.
![mask_only](composition_demo/mask_only.png)
![mask_only](./documentation/composition_demo/mask_only.png)
---
@@ -306,7 +306,7 @@ Just the colored saliency map with no original image.
Black background, saliency values mapped directly to white intensity.
![pure_white](composition_demo/pure_white.png)
![pure_white](./documentation/composition_demo/pure_white.png)
---
@@ -335,43 +335,43 @@ These apply a color gradient to the greyscale saliency values. Low values (less
**cool**
![cool](colormap_demo/cool.png)
![cool](./documentation/colormap_demo/cool.png)
**hot**
![hot](colormap_demo/hot.png)
![hot](./documentation/colormap_demo/hot.png)
**copper**
![copper](colormap_demo/copper.png)
![copper](./documentation/colormap_demo/copper.png)
**inferno**
![inferno](colormap_demo/inferno.png)
![inferno](./documentation/colormap_demo/inferno.png)
**magma**
![magma](colormap_demo/magma.png)
![magma](./documentation/colormap_demo/magma.png)
**ocean**
![ocean](colormap_demo/ocean.png)
![ocean](./documentation/colormap_demo/ocean.png)
**plasma**
![plasma](colormap_demo/plasma.png)
![plasma](./documentation/colormap_demo/plasma.png)
**spring**
![spring](colormap_demo/spring.png)
![spring](./documentation/colormap_demo/spring.png)
**twilight**
![twilight](colormap_demo/twilight.png)
![twilight](./documentation/colormap_demo/twilight.png)
**viridis**
![viridis](colormap_demo/viridis.png)
![viridis](./documentation/colormap_demo/viridis.png)
---
@@ -387,11 +387,11 @@ Default: **0.75** — chosen as the midpoint that preserves enough of the origin
**intensity = 0.5** (lower)
![low_intensity](extras_demo/intensity_0.5.png)
![low_intensity](./documentation/extras_demo/intensity_0.5.png)
**intensity = 1.0** (higher / full)
![high_intensity](extras_demo/intensity_1.0.png)
![high_intensity](./documentation/extras_demo/intensity_1.0.png)
---
@@ -403,11 +403,11 @@ Default: **1.5**.
**contrast = 1.0** (flat, no adjustment)
![low_contrast](extras_demo/contrast_1.0.png)
![low_contrast](./documentation/extras_demo/contrast_1.0.png)
**contrast = 2.0** (more aggressive)
![high_contrast](extras_demo/contrast_2.0.png)
![high_contrast](./documentation/extras_demo/contrast_2.0.png)
---
@@ -419,11 +419,11 @@ Default: **0.25** — chosen to eliminate low-confidence gradient noise while pr
**threshold = 0.0** (nothing filtered)
![low_threshold](extras_demo/threshold_0.png)
![low_threshold](./documentation/extras_demo/threshold_0.png)
**threshold = 0.5** (aggressive filtering)
![high_threshold](extras_demo/threshold_0.5.png)
![high_threshold](./documentation/extras_demo/threshold_0.5.png)
---
@@ -437,11 +437,11 @@ The classification label output of the node always shows what class is actually
**class_index = 1** (a modest override)
![index-1](extras_demo/class_index_1.png)
![index-1](./documentation/extras_demo/class_index_1.png)
**class_index = 10** (a larger displacement)
![index-10](extras_demo/class_index_10.png)
![index-10](./documentation/extras_demo/class_index_10.png)
---
@@ -461,7 +461,7 @@ Default: **true** — the aesthetic improvement is significant and the default s
**use_smoothgrad = false**
![smooth_false](extras_demo/smooth_grad_false.png)
![smooth_false](./documentation/extras_demo/smooth_grad_false.png)
---
@@ -473,7 +473,7 @@ Default: **25**
**samples = 50**
![samples_50](extras_demo/samples_50.png)
![samples_50](./documentation/extras_demo/samples_50.png)
---