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Results from R50 GH action on ubuntu-latest
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‎open/MLCommons/results/gh_ubuntu-latest_x86-mlcommons_cpp-cpu-onnxruntime-default_config/README.md‎

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| Model | Scenario | Accuracy | Throughput | Latency (in ms) |
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|----------|------------|------------|--------------|-------------------|
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| resnet50 | offline | 76 | 23.106 | - |
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| resnet50 | offline | 76 | 21.293 | - |

‎open/MLCommons/results/gh_ubuntu-latest_x86-mlcommons_cpp-cpu-onnxruntime-default_config/resnet50/offline/README.md‎

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* OS version: Linux-6.17.0-1010-azure-x86_64-with-glibc2.39
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* CPU version: x86_64
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* Python version: 3.13.13 (main, Apr 8 2026, 02:25:29) [GCC 13.3.0]
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* Python version: 3.12.13 (main, Mar 4 2026, 02:26:36) [GCC 13.3.0]
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* MLC version: unknown
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## MLC Run Command
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mlc rm cache -f
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mlc pull repo GATEOverflow@mlperf-automations --checkout=07db5b6a0a7f34203663e7c42824ca4ba5f3291e
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mlc pull repo GATEOverflow@mlperf-automations --checkout=9c2f85db548254d709ca80cd7474932d26ee27d5
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```
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`acc`: `76.0`, Required accuracy for closed division `>= 75.6954`
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### Performance Results
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`Samples per second`: `23.1059`
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`Samples per second`: `21.2926`

‎open/MLCommons/results/gh_ubuntu-latest_x86-mlcommons_cpp-cpu-onnxruntime-default_config/resnet50/offline/accuracy/accuracy.txt‎

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accuracy=76.000%, good=380, total=500
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hash=1292243882cd2d64659dfd8b81fe7522e3a740924c22992ba3b921dd4e91c6f4
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hash=79feae106e82c9012a6572f4f82453428bc4b5aba65ce10d99b19b3004ca3100

‎open/MLCommons/results/gh_ubuntu-latest_x86-mlcommons_cpp-cpu-onnxruntime-default_config/resnet50/offline/accuracy/mlperf_log_accuracy.json‎

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