CEML_2020_results

	A	B	C	D	E	F	G	H	I	J	K	L	N	O	P	Q	R
1		Duration	Dataset	Processing	lr_max	train_loss	valid_loss	accuracy	error rate	# of epochs	Base Model	Machine		AVG train_loss	AVG valid_loss	AVG accuracy	AVG error rate
2	Mar 11	1 hour 5 min	plant-id	caney_auto, caney_tight, caney_wide	default	0.35	0.28	0.89	0.1	5	resnet34	Tetra	caney_auto, caney_tight, caney_wide	0.3325	0.2925	0.8875	0.105
3	Mar 11	1 hour 7 min	plant-id	None	default	0.36	0.22	0.92	0.08	5	resnet34	Tetra	None (original set)	0.3725	0.235	0.92	0.0825
4	Mar 11	1 hour 6 min	plant-id	caney_auto, caney_tight, caney_wide	default	0.36	0.29	0.89	0.1	5	resnet34	Tetra	Conclusion: currently applying all three caney detections seems to yield a net negative result. Next test will use more epochs to see if trend continues
5	Mar 11	1 hour 6 min	plant-id	None	default	0.32	0.26	0.92	0.09	5	resnet34	Tetra
6	Mar 11	1 hour 8 min	plant-id	caney_auto, caney_tight, caney_wide	default	0.31	0.3	0.88	0.12	5	resnet34	Tetra
7	Mar 11	1 hour 13 min	plant-id	None	default	0.38	0.22	0.92	0.08	5	resnet34	Tetra
8	Mar 11	1 hour 15 min	plant-id	caney_auto, caney_tight, caney_wide	default	0.31	0.3	0.89	0.1	5	resnet34	Tetra
9	Mar 11	1 hour 16 min	plant-id	None	default	0.43	0.24	0.92	0.08	5	resnet34	Tetra
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11	Date	Duration	Dataset	Processing	lr_max	train_loss	valid_loss	accuracy	error rate	# of epochs	Base Model	Machine		AVG train_loss	AVG valid_loss	AVG accuracy	AVG error rate
12	Mar 11	2.5 hours	plant-id	caney_auto, caney_tight, caney_wide	default	0.3	0.26	0.9	0.09	10	resnet34	Tetra	caney_auto, caney_tight, caney_wide	0.32	0.265	0.9	0.095
13	Mar 11	2.5 hours	plant-id	None	default	0.24	0.21	0.93	0.07	10	resnet34	Tetra	None (original set)	0.25	0.225	0.925	0.075
14	Mar 11	2.5 hours	plant-id	caney_auto, caney_tight, caney_wide	default	0.34	0.27	0.9	0.1	10	resnet34	Tetra	Conclusion: trend continues. Next I will test each individual algorithm without channel compression
15	Mar 11	2.5 hours	plant-id	None	default	0.26	0.24	0.92	0.08	10	resnet34	Tetra
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