Update documentation.

Author: datumbox

docs/source/models.rst

@@ -301,7 +301,7 @@ We provide pre-trained quantized weights for the following models:
 Model                             Acc@1          Acc@5
 ================================  =============  =============
 MobileNet V2                      71.658         90.150
-MobileNet V3 Large                TODO           TODO
+MobileNet V3 Large                73.004         90.858
 ShuffleNet V2                     68.360         87.582
 ResNet 18                         69.494         88.882
 ResNet 50                         75.920         92.814

references/classification/README.md

@@ -101,14 +101,34 @@ For Mobilenet-v2, the model was trained with quantization aware training, the se
 9. momentum: 0.9
 10. lr_step_size:30
 11. lr_gamma: 0.1
+12. weight-decay: 0.0001
 
 ```
-python -m torch.distributed.launch --nproc_per_node=8 --use_env train_quantization.py --model='mobilenetv2'
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train_quantization.py --model='mobilenet_v2'
 ```
 
 Training converges at about 10 epochs.
 
-For post training quant, device is set to CPU. For training, the device is set to CUDA
+For Mobilenet-v3 Large, the model was trained with quantization aware training, the settings used are:
+1. num_workers: 16
+2. batch_size: 32
+3. eval_batch_size: 128
+4. backend: 'qnnpack'
+5. learning-rate: 0.001
+6. num_epochs: 90
+7. num_observer_update_epochs:4
+8. num_batch_norm_update_epochs:3
+9. momentum: 0.9
+10. lr_step_size:30
+11. lr_gamma: 0.1
+12. weight-decay: 0.00001
+
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train_quantization.py --model='mobilenet_v3_large' \
+    --wd 0.00001 --lr 0.001
+```
+
+For post training quant, device is set to CPU. For training, the device is set to CUDA.
 
 ### Command to evaluate quantized models using the pre-trained weights: