1.9 KiB
Model Examples
Keeping everything the same, but changing the model to demonstrate visual differences.
Understanding Models
Models The model is the "brain" being interrogated — it's a pretrained ImageNet classifier whose internal decision-making we're visualizing. Different models have different "personalities" in how they look at images.
need to explain why these models, where they came from. explain reasoning behind choices
VGG16 / VGG19 — Old, deep, sequential architecture from 2014. For portraits, these are the most interesting because they're blunt and spatially crude. The attention patterns feel almost aggressive — big, blocky regions. Good for the "surveillance" feeling you're going for. VGG16 is the default for a reason. ResNet50 / ResNet101 — More modern, uses skip connections. Produces cleaner, more distributed attention. Less visually dramatic than VGG but more "honest" about what it's actually classifying.
DenseNet121 — Dense connectivity means attention is highly distributed across the image. Produces diffuse, almost painterly saliency maps. MobileNetV3 — Designed for mobile devices, so it's aggressively compressed. Produces the sparsest, most stripped-down attention — shows only the absolute minimum the model needs to make a decision. InceptionV3 — Uses parallel convolutions at multiple scales. Produces multi-scale attention that can look almost fractal. Requires input_size 299 instead of 224.
What is it?
Model_name: THIS IS WHAT CHANGES method: guided IG composition: pure_white colormap: magma intensity: 0.75 contrast: 1.5 threshold: 0.25 class_index: -1 input_size: 224 uses_smooth_grad: true snoothgrad_samples: 25
listed out
cool
