Grapevine_Disease_Detection/docs/model_benchmark.csv

1	Model	Size_(MB)	Top1_Accuracy(%)	Top5_Accuracy(%)	Parameters(M)	Depth	Time_(ms)_per_inference_step_(CPU)	Time_(ms)_per_inference_step_(GPU)
2	Xception	88	79.0	94.5	22.9	81	109.4	8.1
3	VGG16	528	71.3	90.1	138.4	16	69.5	4.2
4	VGG19	549	71.3	90.0	143.7	19	84.8	4.4
5	ResNet50	98	74.9	92.1	25.6	107	58.2	4.6
6	ResNet50V2	98	76.0	93.0	25.6	103	45.6	4.4
7	ResNet101	171	76.4	92.8	44.7	209	89.6	5.2
8	ResNet101V2	171	77.2	93.8	44.7	205	72.7	5.4
9	ResNet152	232	76.6	93.1	60.4	311	127.4	6.5
10	ResNet152V2	232	78.0	94.2	60.4	307	107.5	6.6
11	InceptionV3	92	77.9	93.7	23.9	189	42.2	6.9
12	InceptionResNetV2	215	80.3	95.3	55.9	449	130.2	10.0
13	MobileNet	16	70.4	89.5	4.3	55	22.6	3.4
14	MobileNetV2	14	71.3	90.1	3.5	105	25.9	3.8
15	DenseNet121	33	75.0	92.3	8.1	242	77.1	5.4
16	DenseNet169	57	76.2	93.2	14.3	338	96.4	6.3
17	DenseNet201	80	77.3	93.6	20.2	402	127.2	6.7
18	NASNetMobile	23	74.4	91.9	5.3	389	27.0	6.7
19	NASNetLarge	343	82.5	96.0	88.9	533	344.5	20.0
20	EfficientNetB0	29	77.1	93.3	5.3	132	46.0	4.9
21	EfficientNetB1	31	79.1	94.4	7.9	186	60.2	5.6
22	EfficientNetB2	36	80.1	94.9	9.2	186	80.8	6.5
23	EfficientNetB3	48	81.6	95.7	12.3	210	140.0	8.8
24	EfficientNetB4	75	82.9	96.4	19.5	258	308.3	15.1
25	EfficientNetB5	118	83.6	96.7	30.6	312	579.2	25.3
26	EfficientNetB6	166	84.0	96.8	43.3	360	958.1	40.4
27	EfficientNetB7	256	84.3	97.0	66.7	438	1578.9	61.6
28	EfficientNetV2B0	29	78.7	94.3	7.2
29	EfficientNetV2B1	34	79.8	95.0	8.2
30	EfficientNetV2B2	42	80.5	95.1	10.2
31	EfficientNetV2B3	59	82.0	95.8	14.5
32	EfficientNetV2S	88	83.9	96.7	21.6
33	EfficientNetV2M	220	85.3	97.4	54.4
34	EfficientNetV2L	479	85.7	97.5	119.0
35	ConvNeXtTiny	109.42	81.3		28.6
36	ConvNeXtSmall	192.29	82.3		50.2
37	ConvNeXtBase	338.58	85.3		88.5
38	ConvNeXtLarge	755.07	86.3		197.7
39	ConvNeXtXLarge	1310	86.7		350.1