Additional Models

Models that have been verified on Metis but are not yet listed in the Model Zoo with dedicated YAML configurations. You can deploy them by adapting an existing template.

Image Classification

These classification models have been compiled and accuracy-verified on Metis. To use one, copy the mobilenetv4_small-imagenet.yaml template and update the timm_model_args.name field and preprocessing configuration to match your target model.

Model	Accuracy drop vs FP32
`dla34.in1k`	0.59
`dla60.in1k`	0.55
`dla60_res2net.in1k`	0.15
`dla102.in1k`	0.03
`dla169.in1k`	0.27
`efficientnet_es.ra_in1k`	0.02
`efficientnet_es_pruned.in1k`	0.13
`efficientnet_lite0.ra_in1k`	0.22
`dla46_c.in1k`	1.54
`fbnetc_100.rmsp_in1k`	0.24
`gernet_m.idstcv_in1k`	0.05
`gernet_s.idstcv_in1k`	0.18
`mnasnet_100.rmsp_in1k`	0.28
`mobilenetv2_050.lamb_in1k`	0.92
`mobilenetv2_120d.ra_in1k`	0.44
`mobilenetv2_140.ra_in1k`	0.89
`res2net50_14w_8s.in1k`	0.17
`res2net50_26w_4s.in1k`	0.17
`res2net50_26w_6s.in1k`	0.06
`res2net50_48w_2s.in1k`	0.09
`res2net50d.in1k`	0.00
`res2net101_26w_4s.in1k`	0.19
`res2net101d.in1k`	0.08
`resnet10t.c3_in1k`	1.61
`resnet14t.c3_in1k`	0.85
`resnet50c.gluon_in1k`	0.03
`resnet50s.gluon_in1k`	0.19
`resnet101c.gluon_in1k`	0.08
`resnet101d.gluon_in1k`	0.10
`resnet101s.gluon_in1k`	0.18
`resnet152d.gluon_in1k`	0.15
`selecsls42b.in1k`	0.25
`selecsls60.in1k`	0.05
`selecsls60b.in1k`	0.20
`spnasnet_100.rmsp_in1k`	0.25
`tf_efficientnet_es.in1k`	0.26
`tf_efficientnet_lite0.in1k`	0.33
`tf_mobilenetv3_large_minimal_100.in1k`	1.68
`wide_resnet101_2.tv2_in1k`	0.26

Accuracy drop is measured as FP32 top-1 accuracy minus quantized (int8 on AIPU) top-1 accuracy.

Image Classification​

See also​

Image Classification

See also