[WIP] Semantic Segmentation from Dashcam
Ongoing notes, exploration, and development
Created on January 23|Last edited on March 26
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Section 9
Run set
2
Overview
Run set
2
First Pass: Increase Weight Decay, Decrease Learning Rate
107
First manual sweep
10
Hyperparameter Sweep Insights
Run set
398
Manual vs Automated Sweeps
Runs by sweep
398
Per-class accuracies
Run set
5
Resnet v. Alexnet
Run set
2
New Approaches to Semantic Segmentation
How can this model be improved?
- data verification: figure out which dataset items feature humans, and try training/testing on those explicitly
- metrics: use mean IoU across classes--could also split this up per class
- SOTA version: the SOTA model for semantic segmentation is the HRNet. Some other models and code for a semantic segmentation benchmark may also be inspiring.
Literature review
Object-contextual representations
Input size for existing model is [720, 1280], while for HRNet the options are [512, 1024] and [1024, 2048]. Perhaps rescaling is sufficient.
Section 8
Run set
1
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