super-gradients/src/super_gradients/training/Computer_Vision_Models_Pretrained_Checkpoints.md

Computer Vision Models - Pretrained Checkpoints

Pretrained Classification PyTorch Checkpoints

Model	Dataset	Resolution	Top-1	Top-5	Latency (HW)*T4	Latency (Production)**T4	Latency (HW)*Jetson Xavier NX	Latency (Production)**Jetson Xavier NX	Latency Cascade Lake
ViT base	ImageNet21K	224x224	84.15	-	4.46ms	4.60ms	- *	-	57.22ms
ViT large	ImageNet21K	224x224	85.64	-	12.81ms	13.19ms	- *	-	187.22ms
BEiT	ImageNet21K	224x224	-	-	-ms	-ms	- *	-	-ms
EfficientNet B0	ImageNet	224x224	77.62	93.49	0.93ms	1.38ms	- *	-	3.44ms
RegNet Y200	ImageNet	224x224	70.88	89.35	0.63ms	1.08ms	2.16ms	2.47ms	2.06ms
RegNet Y400	ImageNet	224x224	74.74	91.46	0.80ms	1.25ms	2.62ms	2.91ms	2.87ms
RegNet Y600	ImageNet	224x224	76.18	92.34	0.77ms	1.22ms	2.64ms	2.93ms	2.39ms
RegNet Y800	ImageNet	224x224	77.07	93.26	0.74ms	1.19ms	2.77ms	3.04ms	2.81ms
ResNet 18	ImageNet	224x224	70.6	89.64	0.52ms	0.95ms	2.01ms	2.30ms	4.56ms
ResNet 34	ImageNet	224x224	74.13	91.7	0.92ms	1.34ms	3.57ms	3.87ms	7.64ms
ResNet 50	ImageNet	224x224	81.91	93.0	1.03ms	1.44ms	4.78ms	5.10ms	9.25ms
MobileNet V3_large-150 epochs	ImageNet	224x224	73.79	91.54	0.67ms	1.11ms	2.42ms	2.71ms	1.76ms
MobileNet V3_large-300 epochs	ImageNet	224x224	74.52	91.92	0.67ms	1.11ms	2.42ms	2.71ms	1.76ms
MobileNet V3_small	ImageNet	224x224	67.45	87.47	0.55ms	0.96ms	2.01ms *	2.35ms	1.06ms
MobileNet V2_w1	ImageNet	224x224	73.08	91.1	0.46 ms	0.89ms	1.65ms *	1.90ms	1.56ms

NOTE:

• Latency (HW)* - Hardware performance (not including IO)
  
• Latency (Production)** - Production Performance (including IO)
• Performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1
• Performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1

Pretrained Object Detection PyTorch Checkpoints

Model	Dataset	Resolution	mAPval 0.5:0.95	Latency (HW)*T4	Latency (Production)**T4	Latency (HW)*Jetson Xavier NX	Latency (Production)**Jetson Xavier NX	Latency Cascade Lake
SSD lite MobileNet v2	COCO	320x320	21.5	0.77ms	1.40ms	5.28ms	6.44ms	4.13ms
SSD lite MobileNet v1	COCO	320x320	24.3	1.55ms	2.84ms	8.07ms	9.14ms	22.76ms
YOLOX nano	COCO	640x640	26.77	2.47ms	4.09ms	11.49ms	12.97ms	-
YOLOX tiny	COCO	640x640	37.18	3.16ms	4.61ms	15.23ms	19.24ms	-
YOLOX small	COCO	640x640	40.47	3.58ms	4.94ms	18.88ms	22.48ms	-
YOLOX medium	COCO	640x640	46.4	6.40ms	7.65ms	39.22ms	44.5ms	-
YOLOX large	COCO	640x640	49.25	10.07ms	11.12ms	68.73ms	77.01ms	-

NOTE:

• Latency (HW)* - Hardware performance (not including IO)
  
• Latency (Production)** - Production Performance (including IO)
• Latency performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1
• Latency performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1

Pretrained Semantic Segmentation PyTorch Checkpoints

Model	Dataset	Resolution	mIoU	Latency b1T4	Latency b1T4 including IO	Latency (Production)**Jetson Xavier NX
PP-LiteSeg B50	Cityscapes	512x1024	76.48	4.18ms	31.22ms	31.69ms
PP-LiteSeg B75	Cityscapes	768x1536	78.52	6.84ms	33.69ms	49.89ms
PP-LiteSeg T50	Cityscapes	512x1024	74.92	3.26ms	30.33ms	26.20ms
PP-LiteSeg T75	Cityscapes	768x1536	77.56	5.20ms	32.28ms	38.03ms
DDRNet 23 slim	Cityscapes	1024x2048	78.01	5.74ms	32.01ms	45.18ms
DDRNet 23	Cityscapes	1024x2048	80.26	12.74ms	39.01ms	106.26ms
STDC 1-Seg50	Cityscapes	512x1024	75.11	3.34ms	30.12ms	27.54ms
STDC 1-Seg75	Cityscapes	768x1536	77.8	5.53ms	32.490ms	43.88
STDC 2-Seg50	Cityscapes	512x1024	76.44	4.12ms	30.94ms	32.03ms
STDC 2-Seg75	Cityscapes	768x1536	78.93	6.95ms	33.89ms	54.48ms
RegSeg (exp48)	Cityscapes	1024x2048	78.15	12.03ms	38.91ms	78.20ms
Larger RegSeg (exp53)	Cityscapes	1024x2048	79.2	22.00ms	48.96ms	150.78ms

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency), and not including IO

NOTE: For resolutions below 1024x2048 we first resize the input to the inference resolution and then resize the predictions to 1024x2048. The time of resizing is included in the measurements so that the practical input-size is 1024x2048.