[ConvNets/PyT] Minor fixes in README
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Andrzej Sulecki
Krzysztof Kudrynski
parent
99ab780448
commit
b2f30f365f
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@ -326,11 +326,11 @@ unzip
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To run inference on ImageNet, run:
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`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained-from-file -b <batch size> <path to imagenet>`
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`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained -b <batch size> <path to imagenet>`
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To run inference on JPEG image using pre-trained weights, run:
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`python classify.py --arch efficientnet-<version> -c fanin --weights --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch efficientnet-<version> --pretrained --precision AMP|FP32 --image <path to JPEG image>`
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## Advanced
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@ -423,7 +423,7 @@ To run inference on JPEG image, you have to first extract the model weights from
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Then, run the classification script:
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`python classify.py --arch efficientnet-<version> --weights <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch efficientnet-<version> --pretrained-from-file <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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You can also run the ImageNet validation on pretrained weights:
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@ -450,11 +450,11 @@ URL for each model can be found in the following table:
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To run inference on ImageNet, run:
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`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained-from-file -b <batch size> <path to imagenet>`
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`python ./main.py --arch efficientnet-<version> --evaluate --epochs 1 --pretrained -b <batch size> <path to imagenet>`
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To run inference on JPEG images using pretrained weights, run:
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`python classify.py --arch efficientnet-<version> --weights --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch efficientnet-<version> --pretrained --precision AMP|FP32 --image <path to JPEG image>`
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### Quantization process
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@ -487,7 +487,7 @@ There is also a possibility to transform trained models (quantized or not) into
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Quantized models could also be used to classify new images using the `classify.py` flag. For example:
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`python classify.py --arch efficientnet-quant-<version> -c fanin --pretrained-from-file <path to quant weights> --image <path to JPEG image>`
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`python classify.py --arch efficientnet-quant-<version> --pretrained-from-file <path to quant weights> --image <path to JPEG image>`
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## Performance
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@ -297,7 +297,7 @@ To run inference on ImageNet, run:
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To run inference on JPEG image using pretrained weights:
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`python classify.py --arch resnet50 -c fanin --weights nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch resnet50 --pretrained-from-file nvidia_resnet50_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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## Advanced
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@ -472,7 +472,7 @@ To run inference on JPEG image, you have to first extract the model weights from
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Then run classification script:
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`python classify.py --arch resnet50 -c fanin --weights <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch resnet50 --pretrained-from-file <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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You can also run ImageNet validation on pretrained weights:
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@ -276,11 +276,11 @@ unzip resnext101_32x4d_pyt_amp_20.06.0.zip
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To run inference on ImageNet, run:
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`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-weights nvidia_resnext101-32x4d_200821.pth.tar -b <batch size> <path to imagenet>`
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`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-from-file nvidia_resnext101-32x4d_200821.pth.tar -b <batch size> <path to imagenet>`
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To run inference on JPEG image using pretrained weights:
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`python classify.py --arch resnext101-32x4d -c fanin --weights nvidia_resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch resnext101-32x4d --pretrained-from-file nvidia_resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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## Advanced
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@ -319,7 +319,7 @@ usage: main.py [-h] [--data-backend BACKEND] [--arch ARCH]
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[--lr-schedule SCHEDULE] [--warmup E] [--label-smoothing S]
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[--mixup ALPHA] [--momentum M] [--weight-decay W]
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[--bn-weight-decay] [--nesterov] [--print-freq N]
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[--resume PATH] [--pretrained-weights PATH]
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[--resume PATH] [--pretrained-from-file PATH]
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[--static-loss-scale STATIC_LOSS_SCALE] [--dynamic-loss-scale]
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[--prof N] [--amp] [--seed SEED] [--gather-checkpoints]
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[--raport-file RAPORT_FILE] [--evaluate] [--training-only]
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@ -368,7 +368,7 @@ optional arguments:
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--nesterov use nesterov momentum, (default: false)
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--print-freq N, -p N print frequency (default: 10)
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--resume PATH path to latest checkpoint (default: none)
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--pretrained-weights PATH
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--pretrained-from-file PATH
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load weights from here
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--static-loss-scale STATIC_LOSS_SCALE
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Static loss scale, positive power of 2 values can
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@ -420,7 +420,7 @@ Metrics gathered through training:
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To restart training from checkpoint use `--resume` option.
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To start training from pretrained weights (e.g. downloaded from NGC) use `--pretrained-weights` option.
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To start training from pretrained weights (e.g. downloaded from NGC) use `--pretrained-from-file` option.
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The difference between those two is that the pretrained weights contain only model weights,
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and checkpoints, apart from model weights, contain optimizer state, LR scheduler state.
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@ -454,11 +454,11 @@ To run inference on JPEG image, you have to first extract the model weights from
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Then run classification script:
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`python classify.py --arch resnext101-32x4d -c fanin --weights <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch resnext101-32x4d --pretrained-from-file <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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You can also run ImageNet validation on pretrained weights:
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`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-weights <path to pretrained weights> -b <batch size> <path to imagenet>`
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`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-from-file <path to pretrained weights> -b <batch size> <path to imagenet>`
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#### NGC Pretrained weights:
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@ -472,11 +472,11 @@ unzip resnext101_32x4d_pyt_amp_20.06.0.zip
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To run inference on ImageNet, run:
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`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-weights nvidia_resnext101-32x4d_200821.pth.tar -b <batch size> <path to imagenet>`
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`python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --pretrained-from-file nvidia_resnext101-32x4d_200821.pth.tar -b <batch size> <path to imagenet>`
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To run inference on JPEG image using pretrained weights:
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`python classify.py --arch resnext101-32x4d --weights nvidia_resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch resnext101-32x4d --pretrained-from-file nvidia_resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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## Performance
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@ -282,7 +282,7 @@ To run inference on ImageNet, run:
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To run inference on JPEG image using pretrained weights:
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`python classify.py --arch se-resnext101-32x4d -c fanin --weights nvidia_se-resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch se-resnext101-32x4d --pretrained-from-file nvidia_se-resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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## Advanced
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@ -457,7 +457,7 @@ To run inference on JPEG image, you have to first extract the model weights from
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Then run classification script:
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`python classify.py --arch se-resnext101-32x4d -c fanin --weights <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch se-resnext101-32x4d --pretrained-from-file <path to weights from previous step> --precision AMP|FP32 --image <path to JPEG image>`
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You can also run ImageNet validation on pretrained weights:
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@ -478,7 +478,7 @@ To run inference on ImageNet, run:
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To run inference on JPEG image using pretrained weights:
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`python classify.py --arch se-resnext101-32x4d --weights nvidia_se-resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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`python classify.py --arch se-resnext101-32x4d --pretrained-from-file nvidia_se-resnext101-32x4d_200821.pth.tar --precision AMP|FP32 --image <path to JPEG image>`
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## Performance
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