20220712-h16m02s58_example_beans

fxmarty/20220712-h16m02s58_example_beans

20220712-h16m02s58_example_beans is a image classification model from fxmarty. Use it when you need a label for an image.

task: image-classification Backend: sagemaker-training Backend args: {'instancetype': 'ml.g4dn.2xlarge', 'supportedinstructions': None} Number of evaluation samples: All dataset

Downloads · 30 days

Access

Public

Updated Jul 12, 2022

Repo size

6.3 KB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.json116 KB · 79%

At a glance

Task: Image Classification
Access: Public
Created: Jul 12, 2022
Updated: Jul 12, 2022
SHA: 7d060af3

Datasets

beans

Model card

From the Hugging Face model README

task: image-classification
Backend: sagemaker-training
Backend args: {'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}
Number of evaluation samples: All dataset

Fixed parameters:

model_name_or_path: nateraw/vit-base-beans
dataset:
- path: beans
- eval_split: validation
- data_keys: {'primary': 'image'}
- ref_keys: ['labels']
- calibration_split: train
quantization_approach: dynamic
calibration:
- method: minmax
- num_calibration_samples: 100
framework: onnxruntime
framework_args:
- opset: 11
- optimization_level: 1
aware_training: False

Benchmarked parameters:

operators_to_quantize: ['Add'], ['Add', 'MatMul']
node_exclusion: [], ['layernorm', 'gelu', 'residual', 'gather', 'softmax']
per_channel: False, True

Evaluation

Non-time metrics

operators_to_quantize	node_exclusion	per_channel		accuracy (original)	accuracy (optimized)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	0.980	0.980
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	0.980	0.980
`['Add', 'MatMul']`	`[]`	`False`	\|	0.980	0.980
`['Add', 'MatMul']`	`[]`	`True`	\|	0.980	0.980
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	0.980	0.980
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	0.980	0.980
`['Add']`	`[]`	`False`	\|	0.980	0.980
`['Add']`	`[]`	`True`	\|	0.980	0.980

Time metrics

Time benchmarks were run for 15 seconds per config.

Below, time metrics for batch size = 1, input length = 32.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	200.50	63.00	\|	5.00	15.93
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	198.19	72.65	\|	5.07	13.80
`['Add', 'MatMul']`	`[]`	`False`	\|	191.44	63.27	\|	5.27	15.87
`['Add', 'MatMul']`	`[]`	`True`	\|	154.84	72.51	\|	6.47	13.80
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	155.84	130.95	\|	6.47	7.67
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	201.76	131.25	\|	5.00	7.67
`['Add']`	`[]`	`False`	\|	198.96	128.82	\|	5.07	7.80
`['Add']`	`[]`	`True`	\|	163.76	129.62	\|	6.13	7.73

Below, time metrics for batch size = 1, input length = 64.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	162.75	67.18	\|	6.20	14.93
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	159.69	72.77	\|	6.33	13.80
`['Add', 'MatMul']`	`[]`	`False`	\|	183.10	64.02	\|	5.47	15.67
`['Add', 'MatMul']`	`[]`	`True`	\|	157.21	64.16	\|	6.40	15.60
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	155.32	130.74	\|	6.47	7.67
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	198.56	162.51	\|	5.07	6.20
`['Add']`	`[]`	`False`	\|	186.58	163.38	\|	5.40	6.13
`['Add']`	`[]`	`True`	\|	199.75	131.46	\|	5.07	7.67

Below, time metrics for batch size = 1, input length = 128.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	160.58	67.65	\|	6.27	14.80
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	158.60	72.53	\|	6.33	13.80
`['Add', 'MatMul']`	`[]`	`False`	\|	200.46	62.95	\|	5.00	15.93
`['Add', 'MatMul']`	`[]`	`True`	\|	195.39	72.28	\|	5.13	13.87
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	197.59	128.80	\|	5.07	7.80
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	156.24	162.63	\|	6.47	6.20
`['Add']`	`[]`	`False`	\|	157.25	129.13	\|	6.40	7.80
`['Add']`	`[]`	`True`	\|	176.08	161.79	\|	5.73	6.20

Below, time metrics for batch size = 4, input length = 32.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	503.83	219.62	\|	2.00	4.60
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	603.26	266.15	\|	1.67	3.80
`['Add', 'MatMul']`	`[]`	`False`	\|	654.79	217.45	\|	1.53	4.60
`['Add', 'MatMul']`	`[]`	`True`	\|	654.33	219.54	\|	1.53	4.60
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	654.20	481.61	\|	1.53	2.13
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	609.81	632.73	\|	1.67	1.60
`['Add']`	`[]`	`False`	\|	588.86	602.91	\|	1.73	1.67
`['Add']`	`[]`	`True`	\|	666.98	655.32	\|	1.53	1.53

Below, time metrics for batch size = 4, input length = 64.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	656.87	216.32	\|	1.53	4.67
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	507.24	265.62	\|	2.00	3.80
`['Add', 'MatMul']`	`[]`	`False`	\|	655.36	219.61	\|	1.53	4.60
`['Add', 'MatMul']`	`[]`	`True`	\|	613.28	220.96	\|	1.67	4.53
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	656.30	652.72	\|	1.53	1.53
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	521.09	472.90	\|	1.93	2.13
`['Add']`	`[]`	`False`	\|	655.37	473.77	\|	1.53	2.13
`['Add']`	`[]`	`True`	\|	653.62	468.82	\|	1.53	2.13

Below, time metrics for batch size = 4, input length = 128.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	654.24	216.82	\|	1.53	4.67
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	657.16	240.11	\|	1.53	4.20
`['Add', 'MatMul']`	`[]`	`False`	\|	504.14	217.47	\|	2.00	4.60
`['Add', 'MatMul']`	`[]`	`True`	\|	655.94	220.12	\|	1.53	4.60
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	653.99	479.06	\|	1.53	2.13
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	642.48	666.28	\|	1.60	1.53
`['Add']`	`[]`	`False`	\|	656.34	661.24	\|	1.53	1.53
`['Add']`	`[]`	`True`	\|	661.86	472.49	\|	1.53	2.13

Below, time metrics for batch size = 8, input length = 32.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	1294.07	472.54	\|	0.80	2.13
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	1287.58	542.72	\|	0.80	1.87
`['Add', 'MatMul']`	`[]`	`False`	\|	1033.37	433.32	\|	1.00	2.33
`['Add', 'MatMul']`	`[]`	`True`	\|	1030.14	542.36	\|	1.00	1.87
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	953.27	926.14	\|	1.07	1.13
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	1173.01	995.22	\|	0.87	1.07
`['Add']`	`[]`	`False`	\|	1280.07	926.97	\|	0.80	1.13
`['Add']`	`[]`	`True`	\|	1283.70	927.87	\|	0.80	1.13

Below, time metrics for batch size = 8, input length = 64.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	1273.61	435.27	\|	0.80	2.33
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	1157.00	542.75	\|	0.87	1.87
`['Add', 'MatMul']`	`[]`	`False`	\|	968.85	537.65	\|	1.07	1.87
`['Add', 'MatMul']`	`[]`	`True`	\|	1107.66	472.53	\|	0.93	2.13
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	1270.30	1092.10	\|	0.80	0.93
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	1263.29	1012.66	\|	0.80	1.00
`['Add']`	`[]`	`False`	\|	1007.19	1331.12	\|	1.07	0.80
`['Add']`	`[]`	`True`	\|	1286.51	1317.96	\|	0.80	0.80

Below, time metrics for batch size = 8, input length = 128.

operators_to_quantize	node_exclusion	per_channel		latency_mean (original, ms)	latency_mean (optimized, ms)		throughput (original, /s)	throughput (optimized, /s)
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	1188.98	537.58	\|	0.87	1.87
`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	951.31	489.40	\|	1.07	2.07
`['Add', 'MatMul']`	`[]`	`False`	\|	1278.73	537.52	\|	0.80	1.87
`['Add', 'MatMul']`	`[]`	`True`	\|	1005.38	440.01	\|	1.07	2.33
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	\|	1265.55	1304.51	\|	0.80	0.80
`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	\|	1186.54	934.09	\|	0.87	1.13
`['Add']`	`[]`	`False`	\|	1276.38	1319.84	\|	0.80	0.80
`['Add']`	`[]`	`True`	\|	981.81	940.69	\|	1.07	1.07

Task: Image Classification
Created: Jul 12, 2022
Updated: Jul 12, 2022