Merge pull request NVIDIA#920 from NVIDIA/gh/release

Gh/release

Author: nv-kkudrynski

PyTorch/Classification/ConvNets/README.md

@@ -30,7 +30,7 @@ The following table provides links to where you can find additional information
 ## Validation accuracy results
 
 Our results were obtained by running the applicable
-training scripts in the [framework-container-name] NGC container
+training scripts in the 20.12 PyTorch NGC container
 on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
 The specific training script that was run is documented
 in the corresponding model's README.
@@ -56,49 +56,48 @@ three classification models side-by-side.
 
 
 Our results were obtained by running the applicable
-training scripts in the pytorch-20.12 NGC container
+training scripts in the 21.03 PyTorch NGC container
 on NVIDIA DGX A100 with (8x A100 80GB) GPUs.
 Performance numbers (in images per second)
 were averaged over an entire training epoch.
 The specific training script that was run is documented
 in the corresponding model's README.
 
-The following table shows the training accuracy results of the
-three classification models side-by-side.
-
+The following table shows the training accuracy results of
+all the classification models side-by-side.
 
 | **Model** | **Mixed Precision** | **TF32** | **Mixed Precision Speedup** |
 |:----------------------:|:-------------------:|:----------:|:---------------------------:|
-| efficientnet-b0 | 14391 img/s | 8225 img/s | 1.74 x |
-| efficientnet-b4 | 2341 img/s | 1204 img/s | 1.94 x |
-| efficientnet-widese-b0 | 15053 img/s | 8233 img/s | 1.82 x |
-| efficientnet-widese-b4 | 2339 img/s | 1202 img/s | 1.94 x |
-| resnet50 | 15977 img/s | 7365 img/s | 2.16 x |
-| resnext101-32x4d | 7399 img/s | 3193 img/s | 2.31 x |
-| se-resnext101-32x4d | 5248 img/s | 2665 img/s | 1.96 x |
+| efficientnet-b0 | 16652 img/s | 8193 img/s | 2.03 x |
+| efficientnet-b4 | 2570 img/s | 1223 img/s |  2.1 x |
+| efficientnet-widese-b0 | 16368 img/s | 8244 img/s | 1.98 x |
+| efficientnet-widese-b4 | 2585 img/s | 1223 img/s | 2.11 x |
+| resnet50 | 16621 img/s | 7248 img/s | 2.29 x |
+| resnext101-32x4d | 7925 img/s | 3471 img/s | 2.28 x |
+| se-resnext101-32x4d | 5779 img/s | 2991 img/s | 1.93 x |
 
 ### Training performance: NVIDIA DGX-1 16G (8x V100 16GB)
 
 Our results were obtained by running the applicable
-training scripts in the pytorch-20.12 NGC container
+training scripts in the 21.03 PyTorch NGC container
 on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
 Performance numbers (in images per second)
 were averaged over an entire training epoch.
 The specific training script that was run is documented
 in the corresponding model's README.
 
-The following table shows the training accuracy results of the
-three classification models side-by-side.
+The following table shows the training accuracy results of all the
+classification models side-by-side.
 
 | **Model** | **Mixed Precision** | **FP32** | **Mixed Precision Speedup** |
 |:----------------------:|:-------------------:|:----------:|:---------------------------:|
-| efficientnet-b0 | 7664 img/s | 4571 img/s | 1.67 x |
-| efficientnet-b4 | 1330 img/s | 598 img/s | 2.22 x |
-| efficientnet-widese-b0 | 7694 img/s | 4489 img/s | 1.71 x |
-| efficientnet-widese-b4 | 1323 img/s | 590 img/s | 2.24 x |
-| resnet50 | 7608 img/s | 2851 img/s | 2.66 x |
-| resnext101-32x4d | 3742 img/s | 1117 img/s | 3.34 x |
-| se-resnext101-32x4d | 2716 img/s | 994 img/s  | 2.73 x |
+| efficientnet-b0 | 7789 img/s | 4672 img/s | 1.66 x |
+| efficientnet-b4 | 1366 img/s | 616 img/s | 2.21 x |
+| efficientnet-widese-b0 | 7875 img/s | 4592 img/s | 1.71 x |
+| efficientnet-widese-b4 | 1356 img/s | 612 img/s | 2.21 x |
+| resnet50 | 8322 img/s | 2855 img/s | 2.91 x |
+| resnext101-32x4d | 4065 img/s | 1133 img/s | 3.58 x |
+| se-resnext101-32x4d | 2971 img/s | 1004 img/s | 2.95 x |
 
 ## Model Comparison