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‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-onnxruntime_v1.26.0-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 | 22.949 | - |

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-onnxruntime_v1.26.0-default_config/resnet50/offline/README.md‎

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*Check [MLC MLPerf docs](https://docs.mlcommons.org/inference) for more details.*
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## Host platform
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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: 3.12.13 (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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See [MLC installation guide](https://docs.mlcommons.org/inference/install/).
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```bash
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pip install -U mlcflow
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mlc rm cache -f
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mlc pull repo GATEOverflow@mlperf-automations --checkout=0b1d0bff24099eebbd2a419bf9751861c343088a
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```
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*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf,
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you should simply reload GATEOverflow@mlperf-automations without checkout and clean MLC cache as follows:*
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```bash
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mlc rm repo GATEOverflow@mlperf-automations
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mlc pull repo GATEOverflow@mlperf-automations
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mlc rm cache -f
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```
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## Results
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Platform: gh_ubuntu-latest_x86-reference-cpu-onnxruntime_v1.26.0-default_config
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Model Precision: fp32
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### Accuracy Results
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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`: `22.9494`

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-onnxruntime_v1.26.0-default_config/resnet50/offline/accuracy/accuracy.txt‎

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accuracy=76.000%, good=380, total=500
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hash=7622e4516a25dd42229ab7fc2e3784e554434655a7fb009426bb30e0a2d07a6a

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-onnxruntime_v1.26.0-default_config/resnet50/offline/accuracy/mlperf_log_accuracy.json‎

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