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‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-tf_v2.21.0-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 | 19.886 | - |
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| resnet50 | offline | 76 | 21.114 | - |

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-tf_v2.21.0-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: 3.12.13 (main, Mar 4 2026, 02:26:36) [GCC 13.3.0]
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* Python version: 3.13.13 (main, Apr 8 2026, 02:25:29) [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 mlcommons@mlperf-automations --checkout=b80db0891b8bab7f89adfeb0a8d0d5cef9ab36e4
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mlc pull repo GATEOverflow@mlperf-automations --checkout=9c2f85db548254d709ca80cd7474932d26ee27d5
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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 mlcommons@mlperf-automations without checkout and clean MLC cache as follows:*
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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 mlcommons@mlperf-automations
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mlc pull repo mlcommons@mlperf-automations
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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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`acc`: `76.0`, Required accuracy for closed division `>= 75.6954`
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### Performance Results
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`Samples per second`: `19.8864`
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`Samples per second`: `21.1142`

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-tf_v2.21.0-default_config/resnet50/offline/accuracy/accuracy.txt‎

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accuracy=76.000%, good=380, total=500
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hash=52fbcd4d3b1a5687e7e802fe066a2dea4a72923bcb5915c0132a36ca986b16df
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hash=65bb20f9c106f05aa2fc296a4e7210abe2172de0c4ccbf83f102906616b55e45

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-tf_v2.21.0-default_config/resnet50/offline/accuracy/mlperf_log_accuracy.json‎

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