Results (FPGA)

Final Results

Rank	Team Name	Precision	Recall	F1-Score	FPS	Total Score
1	SEUer	0.599	0.434	0.504	480.67	122.098
2	InvolutionNet	0.788	0.483	0.599	236.30	84.785
3	FSBIN	0.661	0.421	0.514	232.59	61.449
4	Puff	0.666	0.405	0.504	232.96	59.176
5	XDD	0.661	0.421	0.514	185.57	49.027
6	PCCC	0.661	0.421	0.514	138.60	36.618
7	approxitrack	0.539	0.245	0.337	164.71	18.706
8	Saecheonnyeon	0.740	0.432	0.545	43.09	12.799
9	bitsplicer	0.609	0.657	0.632	22.67	9.055
10	ADARLAB	0.804	0.601	0.688	7.25	3.432
11	fpgaconvnet	0.611	0.407	0.488	10.86	2.586
12	husky	0.743	0.465	0.572	4.97	1.626

Rank	Team Name	Precision	Recall	F1-Score	FPS	Total Score
1	InvolutionNet	0.716	0.563	0.63	51.36	20.384784
2	ADARLAB	0.806	0.591	0.682	7.61	3.539594
3	bitsplicer	0.653	0.672	0.662	7.82	3.427068
4	Puff	0.625	0.471	0.537	2.29	0.660365
5	Log_gehts	0.01	0.013	0.011	3.39	0.00041
6	saecheonnyeon	0.004	0.002	0.002	15.63	0.000063
7	x2_team	0.004	0.002	0.003	3.49	0.000031

Did not run successfully:

FSBIN: ValueError: could not broadcast input array from shape (544,960,3) into shape (384,640,3) on line: image_input[0,...] = input
HLSPoliTo: IndexError: list index out of range on line: pred = non_max_suppression_custom(pred)[0])
NewLab: Board crashes on first batch. I reduced the batch size to 108, but now get an error UnboundLocalError: local variable 'i' referenced before assignment on line last_name = [k[0].name for k in rgb_imgs[i : BATCH_SIZE + i]]

Note: These results were accidentally collected on the full set of images (training + testing sets), so accuracy is higher than it should be.

Rank	Team Name	Precision	Recall	F1-Score	FPS	Total Score
1	InvolutionNet	0.803	0.709	0.753	4.93	2.795354
2	FSBIN	0.015	0.003	0.005	2.3	0.000058