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timm/poolformer_m48.sail_in1k

Embedl-optimized
timm poolformer_m48.sail_in1k architecture graph
timm poolformer_m48.sail_in1k architecture graph
  • timm/poolformer_m48.sail_in1k
  • INT8
  • AGX Thor
Labels
  • Image
  • Classification
Build
Embedl-optimized
Precision
INT8
Runtime
TensorRT 10.16.2.10 with Embedl optimizations
Input size
224×224
Run poolformer_m48.sail_in1k on AGX Thor

poolformer_m48.sail_in1k is an image classifier from timm. It labels an image with one of the 1,000 ImageNet classes.

License: apache-2.0 (the upstream model's).

Methodology

  • Latency: trtexec on the NVIDIA Jetson AGX Thor: batch 1, 500 warm-up runs, then 200 timed; GPU compute is the engine's time on the GPU, without copies to and from it.
  • Accuracy: the ImageNet validation set, 50,000 images, on the L4 build's INT8 engine. This page's engine is compiled from the same quantized ONNX, so its accuracy is the same up to the device's kernels, which the self-test checks.
  • Self-test: during the build, on the Thor: the runtime's own `embedl info`, `benchmark`, `infer` and its KServe server, and on the L4 build's example images each engine's top class must be in the other's top 3 (they matched exactly on 3 of 3).
  • Build: the L4 build's quantized ONNX (Embedl Deploy, INT8 Q/DQ), compiled with TensorRT 10.16.2.10 on the device itself.

Files

Image Size

Model

docker.io/embedl/model-artifacts:poolformer_m48.sail_in1k-timm_int8_embedl_tensorrt_thor.b6bd5e10f36b1b8e

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Runtime

docker.io/embedl/models:runtimes.tensorrt.arm64.2026101815014ee0

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