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Endpoint Error Results Summary

Exp	01	02	03	04	05	06	07	08	09	10	11	12	Comp	Eval	Note
001	0.07	0.07	0.08	0.09	0.15	0.14	0.11	0.13	0.18	0.22	0.26	0.26		Good	The simplest baseline
002	0.01	0.01	0.03	0.06	0.08	0.04	0.04	0.03	0.12	0.19	0.18	0.12	001	Good	Moving pixel occlude static pixel
003	0.00	0.01	0.02	0.05	0.05	0.03	0.27	0.33	1.68	1.74	0.15	0.33	002	Bad	New appear pixel not in loss
004	0.01	0.01	0.03	0.06	0.09	0.05	0.03	0.03	0.13	0.17	0.17	0.10	002	Good	Decompose x and y
005-1	0.04	0.06	0.06	0.06	0.15	0.11	0.10	0.11	0.17	0.17	----	----	002	Bad	Neural net predict disappear
005	0.19	0.17	0.26	0.20	0.12	0.20	0.43	0.69	1.87	2.25	0.81	0.56	003	Bad	Neural net predict disappear
006	0.02	0.02	0.02	0.04	0.06	0.05	0.08	0.03	0.06	0.10	0.15	0.12	005-1	Good	Old pixel loss divided by total number of old pixels
006-1	0.00	0.01	0.02	0.05	0.05	0.03	0.01	0.02	0.10	0.19	0.09	0.08	002	Good	Old pixel loss divided by total number of old pixels
007	0.02	0.02	0.04	0.05	0.07	0.05	0.08	0.09	0.13	0.15	0.19	0.18	006	Bad	Use avearge value at occlusion
008	0.01	0.01	0.00	0.03	0.07	0.04	0.03	0.09	0.05	0.06	0.12	0.97	006	Bad	New appear and occlude location both not in loss
008-1	0.01	0.01	0.01	0.03	0.05	0.03	0.03	0.04	0.12	0.05	0.12	0.13	008	Good	Extra loss for total number of new and conflicting pixels
009	0.12	0.01	0.01	0.03	0.06	0.04	0.01	0.12	0.04	0.07	0.14	0.83	008	Bad	Decompose x and y
010	0.08	0.01	0.00	0.02	0.05	0.04	0.05	0.22	0.28	0.05	0.13	0.94	008	Bad	Wider network, proves exp008 is bad
011	0.01	0.01	0.02	0.04	0.06	0.04	0.04	0.03	0.07	0.12	0.12	0.11	006	Good	Wider network
012	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	000
013	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	000
014	0.04	0.05	0.03	0.06	0.13	0.09	0.09	0.09	0.14	0.14	0.27	0.20	001	Good	Predict relative depth
015	0.02	0.03	0.03	0.03	0.15	0.05	0.11	0.12	0.04	0.06	0.17	0.38	014	Good	Old pixel loss divided by total number of old pixels
016	0.02	0.04	0.04	0.06	0.08	0.07	0.07	0.11	0.11	0.22	0.19	0.19	014	Bad	Bidirectional model
017	0.04	0.03	0.04	0.04	0.12	0.08	0.09	0.09	0.04	0.20	0.28	0.43	015	Bad	Add a few more layers at the bottom of neural net
018	0.05	0.04	0.04	0.02	0.08	0.03	0.12	0.16	0.09	0.06	0.13	0.38	015	Bad	Predict depth using only one image
019	0.00	0.05	0.00	0.06	0.07	0.07	0.01	0.07	0.02	0.06	0.22	0.21	018	Good	Add segmentation temporal consistency loss
020	0.00	0.04	0.01	0.03	0.09	0.08	0.03	0.07	0.02	0.06	0.20	0.18	019	Good	Bidirectional model
021	0.00	0.01	0.00	0.02	0.06	0.03	0.02	0.03	0.01	0.05	0.22	0.17	019	Good	Add flow smoothness loss
000	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	000

Exp	01	02	03	04	05	06	07	08	09	10	11	12	Comp	Eval	Note
001	0.07	0.07	0.08	0.09	0.15	0.14	0.11	0.13	0.18	0.22	0.26	0.26		Good	The simplest baseline
002	0.01	0.01	0.03	0.06	0.08	0.04	0.04	0.03	0.12	0.19	0.18	0.12	001	Good	Moving pixel occlude static pixel
006-1	0.00	0.01	0.02	0.05	0.05	0.03	0.01	0.02	0.10	0.19	0.09	0.08	002	Good	Old pixel loss divided by total number of old pixels
021	0.00	0.01	0.00	0.02	0.06	0.03	0.02	0.03	0.01	0.05	0.22	0.17	019	Good	Add flow smoothness loss

Take Home Message

We should use: new appear pixel not in loss, loss divided by total number of old pixels

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
data		data
exp001		exp001
exp002		exp002
exp003		exp003
exp004		exp004
exp005-1		exp005-1
exp005		exp005
exp006-1		exp006-1
exp006		exp006
exp007		exp007
exp008-1		exp008-1
exp008		exp008
exp009		exp009
exp010		exp010
exp011		exp011
exp012		exp012
exp013		exp013
exp014		exp014
exp015		exp015
exp016		exp016
exp017		exp017
exp018		exp018
exp019		exp019
exp020		exp020
exp021		exp021
exp022		exp022
visualize		visualize
.gitignore		.gitignore
README.md		README.md
__init__.py		__init__.py
base_demo.py		base_demo.py
base_model.py		base_model.py
data.png		data.png
learning_args.py		learning_args.py
plot_curve.py		plot_curve.py
plot_curve1.sh		plot_curve1.sh
run1.sh		run1.sh
run2.sh		run2.sh
run_all.sh		run_all.sh
set_path.sh		set_path.sh
test_0.png		test_0.png
test_gt.png		test_gt.png
trim.sh		trim.sh
unit_test.sh		unit_test.sh
unit_test_bidirect.sh		unit_test_bidirect.sh

yangyi02/video_motion_synthetic2

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Endpoint Error Results Summary

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