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docs(task/seg): add final training results for SNP target

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piero2c 2023-04-03 07:40:29 -07:00
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tasks/face_segmentation/README.md
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@ -79,24 +79,29 @@ The table below shows the final results after fully training the final pareto ar
![pareto_evolution](assets/snp_pareto_evolution.png)
The selected architectures for the search with the `snp_search.yaml` config file can be found in the [archs/snp_target/](arch/snp_target/) directory or in the table below.
| Architecture | Search iteration | SNP Quantized Latency (s) | Partial Training Val. IOU |
|:-----------------------------------------------------------------------------------------------------------|----------------:|----------------------------:|----------------------------:|
| [69f28a4c45aef58a67e2e2e0ce2d087b60b03173](archs/snp_target/69f28a4c45aef58a67e2e2e0ce2d087b60b03173.json) | 12 | 0.008 | 0.783 |
| [b14a1f0a3d17ea0f62022c2cf61da032fd7c9971](archs/snp_target/b14a1f0a3d17ea0f62022c2cf61da032fd7c9971.json) | 5 | 0.007 | 0.769 |
| [946fb0e27ef6ab9659b128006697a1b5a90e674c](archs/snp_target/946fb0e27ef6ab9659b128006697a1b5a90e674c.json) | 13 | 0.007 | 0.768 |
| [7bd6a76ec04e9f85c27d69a48557f689b0af2037](archs/snp_target/7bd6a76ec04e9f85c27d69a48557f689b0af2037.json) | 5 | 0.006 | 0.761 |
| [fb5511d6bee3bf52abed1527850c829cc4293098](archs/snp_target/fb5511d6bee3bf52abed1527850c829cc4293098.json) | 7 | 0.005 | 0.758 |
| [4fca939c89bf725f9efa47606c640e302f8ae9cc](archs/snp_target/4fca939c89bf725f9efa47606c640e302f8ae9cc.json) | 10 | 0.004 | 0.752 |
| [0ef9945b08c953586848a8507bc5d057fab7278d](archs/snp_target/0ef9945b08c953586848a8507bc5d057fab7278d.json) | 14 | 0.004 | 0.749 |
| [d47fc530a155c9c182773fc918fc3f17ed27a9d5](archs/snp_target/d47fc530a155c9c182773fc918fc3f17ed27a9d5.json) | 13 | 0.003 | 0.712 |
| [2aa378e5fad84ecc2114f8855a2cd8b02658cbdc](archs/snp_target/2aa378e5fad84ecc2114f8855a2cd8b02658cbdc.json) | 14 | 0.003 | 0.709 |
| [81f407d6f62de129e917c6b4f58021143a5df050](archs/snp_target/81f407d6f62de129e917c6b4f58021143a5df050.json) | 7 | 0.003 | 0.703 |
| [a223144f3b12adf3144478e5060bd99ef2a64ae9](archs/snp_target/a223144f3b12adf3144478e5060bd99ef2a64ae9.json) | 13 | 0.003 | 0.693 |
| [206e6e499eca01389b46c46989588ff04a2f3a42](archs/snp_target/206e6e499eca01389b46c46989588ff04a2f3a42.json) | 14 | 0.003 | 0.688 |
| [115fc8c962797a6dfd9c3f24fd5ccb4b60df95df](archs/snp_target/115fc8c962797a6dfd9c3f24fd5ccb4b60df95df.json) | 10 | 0.003 | 0.682 |
| [230f1fe115fac89432f5bccad7a01c65e3bb2918](archs/snp_target/230f1fe115fac89432f5bccad7a01c65e3bb2918.json) | 10 | 0.003 | 0.666 |
| [78c76774f378e083c788e56e86978f6d1d9f267c](archs/snp_target/78c76774f378e083c788e56e86978f6d1d9f267c.json) | 10 | 0.003 | 0.659 |
| [604ee54bcc767722bbdd3a610246aadca5a32214](archs/snp_target/604ee54bcc767722bbdd3a610246aadca5a32214.json) | 11 | 0.003 | 0.657 |
| [c570e333fd94f2d514eb1955fafc9eeeb012e750](archs/snp_target/c570e333fd94f2d514eb1955fafc9eeeb012e750.json) | 9 | 0.003 | 0.636 |
| [4786c03a18be281ad2fed235c86a5fe952fb4b0a](archs/snp_target/4786c03a18be281ad2fed235c86a5fe952fb4b0a.json) | 9 | 0.002 | 0.562 |
### Final Training
The table below shows the final results after fully training the final pareto architectures for 30 epochs using the [train.py](./train.py) script.
| Architecture | Search iteration | SNP Quantized Latency (s) | Partial Training Val. IOU | Full training Validation mIOU |
|:-----------------------------------------------------------------------------------------------------------|--------------------:|-----------------------------:|-----------------------------:|------------------:|
| [b14a1f0a3d17ea0f62022c2cf61da032fd7c9971](archs/snp_target/b14a1f0a3d17ea0f62022c2cf61da032fd7c9971.json) | 5 | 0.007 | 0.769 | 0.88 |
| [946fb0e27ef6ab9659b128006697a1b5a90e674c](archs/snp_target/946fb0e27ef6ab9659b128006697a1b5a90e674c.json) | 13 | 0.007 | 0.768 | 0.87 |
| [69f28a4c45aef58a67e2e2e0ce2d087b60b03173](archs/snp_target/69f28a4c45aef58a67e2e2e0ce2d087b60b03173.json) | 12 | 0.008 | 0.783 | 0.87 |
| [7bd6a76ec04e9f85c27d69a48557f689b0af2037](archs/snp_target/7bd6a76ec04e9f85c27d69a48557f689b0af2037.json) | 5 | 0.006 | 0.761 | 0.87 |
| [fb5511d6bee3bf52abed1527850c829cc4293098](archs/snp_target/fb5511d6bee3bf52abed1527850c829cc4293098.json) | 7 | 0.005 | 0.758 | 0.86 |
| [4fca939c89bf725f9efa47606c640e302f8ae9cc](archs/snp_target/4fca939c89bf725f9efa47606c640e302f8ae9cc.json) | 10 | 0.004 | 0.752 | 0.86 |
| [0ef9945b08c953586848a8507bc5d057fab7278d](archs/snp_target/0ef9945b08c953586848a8507bc5d057fab7278d.json) | 14 | 0.004 | 0.749 | 0.85 |
| [81f407d6f62de129e917c6b4f58021143a5df050](archs/snp_target/81f407d6f62de129e917c6b4f58021143a5df050.json) | 7 | 0.003 | 0.703 | 0.84 |
| [d47fc530a155c9c182773fc918fc3f17ed27a9d5](archs/snp_target/d47fc530a155c9c182773fc918fc3f17ed27a9d5.json) | 13 | 0.003 | 0.712 | 0.84 |
| [2aa378e5fad84ecc2114f8855a2cd8b02658cbdc](archs/snp_target/2aa378e5fad84ecc2114f8855a2cd8b02658cbdc.json) | 14 | 0.003 | 0.709 | 0.84 |
| [a223144f3b12adf3144478e5060bd99ef2a64ae9](archs/snp_target/a223144f3b12adf3144478e5060bd99ef2a64ae9.json) | 13 | 0.003 | 0.693 | 0.83 |
| [115fc8c962797a6dfd9c3f24fd5ccb4b60df95df](archs/snp_target/115fc8c962797a6dfd9c3f24fd5ccb4b60df95df.json) | 10 | 0.003 | 0.682 | 0.83 |
| [206e6e499eca01389b46c46989588ff04a2f3a42](archs/snp_target/206e6e499eca01389b46c46989588ff04a2f3a42.json) | 14 | 0.003 | 0.688 | 0.83 |
| [230f1fe115fac89432f5bccad7a01c65e3bb2918](archs/snp_target/230f1fe115fac89432f5bccad7a01c65e3bb2918.json) | 10 | 0.003 | 0.666 | 0.82 |
| [78c76774f378e083c788e56e86978f6d1d9f267c](archs/snp_target/78c76774f378e083c788e56e86978f6d1d9f267c.json) | 10 | 0.003 | 0.659 | 0.82 |
| [604ee54bcc767722bbdd3a610246aadca5a32214](archs/snp_target/604ee54bcc767722bbdd3a610246aadca5a32214.json) | 11 | 0.003 | 0.657 | 0.82 |
| [c570e333fd94f2d514eb1955fafc9eeeb012e750](archs/snp_target/c570e333fd94f2d514eb1955fafc9eeeb012e750.json) | 9 | 0.003 | 0.636 | 0.80 |
| [4786c03a18be281ad2fed235c86a5fe952fb4b0a](archs/snp_target/4786c03a18be281ad2fed235c86a5fe952fb4b0a.json) | 9 | 0.002 | 0.562 | 0.79 |