In this repository you will find implementations of various image classification models.
The following table provides links to where you can find additional information on each model:
| Model | Link |
|---|---|
| resnet50 | README |
| resnext101-32x4d | README |
| se-resnext101-32x4d | README |
Our results were obtained by running the applicable training scripts in the [framework-container-name] NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. The specific training script that was run is documented in the corresponding model's README.
The following table shows the validation accuracy results of the three classification models side-by-side.
| arch | AMP Top1 | AMP Top5 | FP32 Top1 | FP32 Top1 |
|---|---|---|---|---|
| resnet50 | 78.46 | 94.15 | 78.50 | 94.11 |
| resnext101-32x4d | 80.08 | 94.89 | 80.14 | 95.02 |
| se-resnext101-32x4d | 81.01 | 95.52 | 81.12 | 95.54 |
Our results were obtained by running the applicable training scripts in the pytorch-19.10 NGC container on NVIDIA DGX-1 with (8x V100 16G) 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.
| arch | Mixed Precision | FP32 | Mixed Precision speedup |
|---|---|---|---|
| resnet50 | 6888.75 img/s | 2945.37 img/s | 2.34x |
| resnext101-32x4d | 2384.85 img/s | 1116.58 img/s | 2.14x |
| se-resnext101-32x4d | 2031.17 img/s | 977.45 img/s | 2.08x |
Our results were obtained by running the applicable training scripts in the pytorch-19.10 NGC container on NVIDIA DGX-2 with (16x V100 32G) 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.
| arch | Mixed Precision | FP32 | Mixed Precision speedup |
|---|---|---|---|
| resnet50 | 13443.82 img/s | 6263.41 img/s | 2.15x |
| resnext101-32x4d | 4473.37 img/s | 2261.97 img/s | 1.98x |
| se-resnext101-32x4d | 3776.03 img/s | 1953.13 img/s | 1.93x |
Plot describes relationship between floating point operations needed for computing forward pass on a 224px x 224px image, for the implemented models. Dot size indicates number of trainable parameters.
Plot describes relationship between inference latency, throughput and batch size for the implemented models.