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Results from R50 GH action on ubuntu-latest
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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 | 21.114 | - |
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| resnet50 | offline | 76 | 19.77 | - |

‎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: 3.13.13 (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=9c2f85db548254d709ca80cd7474932d26ee27d5
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mlc pull repo mlcommons@mlperf-automations --checkout=d71465f5b4dafcd0352ec15ac9f017292fee7a65
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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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you should simply reload mlcommons@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 repo mlcommons@mlperf-automations
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mlc pull repo mlcommons@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`: `21.1142`
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`Samples per second`: `19.7698`

‎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=65bb20f9c106f05aa2fc296a4e7210abe2172de0c4ccbf83f102906616b55e45
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hash=bc80d1de10659d53b92111b00249d5d865a7c92f28eb041dfe95f6da7bc618c8

‎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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