Add some info to README

Author: rwightman

README.md

@@ -36,6 +36,62 @@ I've included a few of my favourite models, but this is not an exhaustive collec
     * MobileNet-V3 (work in progress, validating config)
     * ChamNet (details hard to find, currently an educated guess)
     * FBNet-C (TODO A/B variants)
+    
+The full list of model strings that can be passed to model factory via `--model` arg for train, validation, inference scripts:
+```
+chamnetv1_100
+chamnetv2_100
+densenet121
+densenet161
+densenet169
+densenet201
+dpn107
+dpn131
+dpn68
+dpn68b
+dpn92
+dpn98
+fbnetc_100
+inception_resnet_v2
+inception_v4
+mnasnet_050
+mnasnet_075
+mnasnet_100
+mnasnet_140
+mnasnet_small
+mobilenetv1_100
+mobilenetv2_100
+mobilenetv3_050
+mobilenetv3_075
+mobilenetv3_100
+pnasnet5large
+resnet101
+resnet152
+resnet18
+resnet34
+resnet50
+resnext101_32x4d
+resnext101_64x4d
+resnext152_32x4d
+resnext50_32x4d
+semnasnet_050
+semnasnet_075
+semnasnet_100
+semnasnet_140
+seresnet101
+seresnet152
+seresnet18
+seresnet34
+seresnet50
+seresnext101_32x4d
+seresnext26_32x4d
+seresnext50_32x4d
+spnasnet_100
+tflite_mnasnet_100
+tflite_semnasnet_100
+xception
+```
+
 ## Features
 Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:
 * All models have a common default configuration interface and API for
@@ -72,6 +128,30 @@ I've leveraged the training scripts in this repository to train a few of the mod
 
 NOTE: For some reason I can't hit the stated accuracy with my impl of MNASNet and Google's tflite weights. Using a TF equivalent to 'SAME' padding was important to get > 70%, but something small is still missing. Trying to train my own weights from scratch with these models has so far to leveled off in the same 72-73% range.
 
+## Script Usage
+
+### Training
+
+The variety of training args is large and not all combinations of options (or even options) have been fully tested. For the training dataset folder, specify the folder to the base that contains a `train` and `validation` folder.
+
+To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value:
+
+`./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --repp --batch-size 256 -j 4`
+
+NOTE: NVIDIA APEX should be installed to run in per-process distributed via DDP or to enable AMP mixed precision with the --amp flag
+ 
+### Validation / Inference
+
+Validation and inference scripts are similar in usage. One outputs metrics on a validation set and the other outputs topk class ids in a csv. Specify the folder containing validation images, not the base as in training script. 
+
+To validate with the model's pretrained weights (if they exist):
+
+`python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained`
+
+To run inference from a checkpoint:
+
+`python inference.py /imagenet/validation/ --model mobilenetv3_100 --checkpoint ./output/model_best.pth.tar`
+
 ## TODO
 A number of additions planned in the future for various projects, incl
 * Find optimal training hyperparams and create/port pretraiend weights for the generic MobileNet variants