Table 3. HierbaNetV1 model summary of convolutional layers.

Layer#	Layer name	Output shape	Parameters	KernelSize	Filters	Growthrate of featuremaps
1	Base_Conv1 (Conv2D)	(None, 224, 224, 32)	896	3 × 3	32	32
5	B1_HL_Conv1 (Conv2D)	(None, 112, 112, 64)	2,112	1 × 1	64	96
6	B1_HL_Conv2 (Conv2D)	(None, 112, 112, 64)	18,496	3 × 3	64	160
7	B1_HL_Conv3 (Conv2D)	(None, 112, 112, 64)	51,264	5 × 5	64	224
8	B1_HL_Conv4 (Conv2D)	(None, 112, 112, 64)	100,416	7 × 7	64	288
18	B1_HL_Conv5 (Conv2D)	(None, 112, 112, 256)	590,080	3 × 3	256	544
21	B1_HL_Conv6 (Conv2D)	(None, 112, 112, 256)	590,080	3 × 3	256	800
24	B1_HL_Conv7 (Conv2D)	(None, 112, 112, 512)	1,180,160	3 × 3	512	1,312
25	B1_LL_Conv1 (Conv2D)	(None, 112, 112, 64)	18,496	3 × 3	64	1,376
33	Base_Conv2 (Conv2D)	(None, 56, 56, 576)	2,986,560	3 × 3	576	1,952
37	B2_HL_Conv1 (Conv2D)	(None, 28, 28, 64)	36,928	1 × 1	64	2,016
38	B2_HL_Conv2 (Conv2D)	(None, 28, 28, 64)	331,840	3 × 3	64	2,080
39	B2_HL_Conv3 (Conv2D)	(None, 28, 28, 64)	921,664	5 × 5	64	2,144
40	B2_HL_Conv4 (Conv2D)	(None, 28, 28, 64)	1,806,400	7 × 7	64	2,208
50	B2_HL_Conv5 (Conv2D)	(None, 28, 28, 256)	590,080	3 × 3	256	2,464
53	B2_HL_Conv6 (Conv2D)	(None, 28, 28, 256)	590,080	3 × 3	256	2,720
56	B2_HL_Conv7 (Conv2D)	(None, 28, 28, 512)	1,180,160	3 × 3	512	3,232
57	B2_LL_Conv1 (Conv2D)	(None, 28, 28, 64)	331,840	3 × 3	64	3,296
65	Base_Conv3 (Conv2D)	(None, 14, 14, 576)	2,986,560	3 × 3	576	3,872
Parametric details of the 72 layers in HierbaNetV1
Total params: 14,331,331
Trainable params: 14,323,587
Non-trainable params: 7,744
Total feature maps generated: 3,872