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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 | 21.176 | - |
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| resnet50 | offline | 76 | 21.054 | - |

‎open/MLCommons/results/gh_ubuntu-latest_x86-reference-cpu-tf_v2.21.0-default_config/resnet50/offline/README.md‎

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@@ -16,16 +16,16 @@ pip install -U mlcflow
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mlc rm cache -f
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mlc pull repo mlcommons@mlperf-automations --checkout=fcf34169cc7c87383ce326d8a2cb64a6fd0b97f6
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mlc pull repo GATEOverflow@mlperf-automations --checkout=4e55476fb1324b45955057790b26baefef4aa3f1
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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`: `21.1762`
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`Samples per second`: `21.0541`

‎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=bbd05e4aec6985733e2732005c6140a414792f21bda7638cc51f935d82f09802
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hash=f92528de4827fba98d05a035af907a2ced55b1091454c7514c441ea71c210d54

‎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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