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Additional eval7 baselines #1959

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Sep 22, 2023
Merged

Additional eval7 baselines #1959

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58f3992
MOT results with updated model kwargs
swsuggs Jul 12, 2023
1a77e10
OD results with updated kwargs and targeted attack
swsuggs Jul 12, 2023
bef1f26
poison results for skull and student-driver triggers
swsuggs Jul 12, 2023
c5d0c04
add model kwargs to targeted carla configs
swsuggs Jul 12, 2023
02d57fd
carla od test splits
swsuggs Aug 2, 2023
484ea3a
carla od again
swsuggs Sep 21, 2023
2539af1
Merge branch 'develop' of https://github.com/twosixlabs/armory into a…
swsuggs Sep 21, 2023
6278dfd
link to configs and triggers
swsuggs Sep 22, 2023
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16 changes: 8 additions & 8 deletions docs/baseline_results/carla_mot_results.md
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This is the baseline evaluation for the multi-object tracking scenario. For single-object tracking, see [carla_video_tracking_results.md](../baseline_results/carla_video_tracking_results.md).

For [dev data](https://github.com/twosixlabs/armory/blob/master/armory/data/adversarial/carla_mot_dev.py), results obtained using Armory v0.16.1.
[Test](https://github.com/twosixlabs/armory/blob/master/armory/data/adversarial/carla_mot_test.py) results obtained using Armory v0.17.2.
Results obtained using Armory v0.18.0.



| Data | Defended | Attack | Attack Parameters | Benign DetA / AssA / HOTA | Adversarial DetA / AssA / HOTA | Test Size |
|------|----------|-------------------|--------------------------------|---------------------------|--------------------------------|-----------|
| Dev | no | Adversarial Patch | step_size=0.02, max_iter=100 | 0.49 / 0.62 / 0.55 | 0.18 / 0.57 / 0.32 | 20 |
| Dev | no | Robust DPatch | step_size=0.002, max_iter=1000 | 0.49 / 0.62 / 0.55 | 0.39 / 0.59 / 0.48 | 20 |
| Dev | yes | Robust DPatch | step_size=0.002, max_iter=1000 | 0.34 / 0.53 / 0.42 | 0.24 / 0.51 / 0.34 | 20 |
| Test | no | Adversarial Patch | step_size=0.02, max_iter=100 | 0.43 / 0.51 / 0.46 | 0.19 / 0.45 / 0.29 | 10 |
| Test | no | Robust DPatch | step_size=0.002, max_iter=1000 | 0.43 / 0.51 / 0.46 | 0.31 / 0.46 / 0.37 | 10 |
| Test | yes | Robust DPatch | step_size=0.002, max_iter=1000 | 0.32 / 0.45 / 0.38 | 0.22 / 0.41 / 0.30 | 10 |
| Dev | no | Adversarial Patch | step_size=0.02, max_iter=100 | 0.55 / 0.64 / 0.59 | 0.15 / 0.58 / 0.29 | 20 |
| Dev | no | Robust DPatch | step_size=0.002, max_iter=1000 | 0.55 / 0.64 / 0.59 | 0.42 / 0.61 / 0.50 | 20 |
| Dev | yes | Robust DPatch | step_size=0.002, max_iter=1000 | 0.36 / 0.53 / 0.44 | 0.25 / 0.49 / 0.35 | 20 |
| Test | no | Adversarial Patch | step_size=0.02, max_iter=100 | 0.45 / 0.55 / 0.49 | 0.25 / 0.47 / 0.35 | 10 |
| Test | no | Robust DPatch | step_size=0.002, max_iter=1000 | 0.45 / 0.55 / 0.49 | 0.36 / 0.49 / 0.41 | 10 |
| Test | yes | Robust DPatch | step_size=0.002, max_iter=1000 | 0.31 / 0.44 / 0.37 | 0.22 / 0.39 / 0.29 | 10 |

Defended results not available for Adversarial Patch attack because JPEG Compression defense is not implemented in PyTorch and so is not fully differentiable.
Note that Robust DPatch is considerably slower than Adversarial Patch.
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