Update README.md

README.md

@@ -313,14 +313,31 @@ CUDA_VISIBLE_DEVICES=0 python run_qa.py \
 We conduct Neural Architecture Search (NAS) on the ResNet architecture using the ImageNet dataset. You can run the following command:
 
 ```train
-cd neural-architecture-search
+cd ../neural-architecture-search
 
 torchrun --nproc_per_node=4 train.py\
-  --data-path /path/to/imagenet/\
-  --model resnet18 --output-dir resnet18 --weights ResNet18_Weights.IMAGENET1K_V1\
-  --batch-size 64 --epochs 10 --lr 0.0004 --lr-step-size 2 --lr-gamma 0.5\
-  --lr-warmup-method constant --lr-warmup-epochs 1 --lr-warmup-decay 0. \
-  --apply-trp --trp-lambdas 0.1 0.01 --print-freq 100
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model resnet18 --output-dir resnet18 --weights ResNet18_Weights.IMAGENET1K_V1\
+    --batch-size 128 --epochs 10 --lr 0.0004 --lr-step-size 2 --lr-gamma 0.5\
+    --lr-warmup-method constant --lr-warmup-epochs 1 --lr-warmup-decay 0.\
+    --apply-trp --trp-depths 3 3 3 --trp-planes 256 --trp-lambdas 0.4 0.2 0.1 --print-freq 100
+
+# ✅ Test:  Acc@1 73.900 Acc@5 91.536
+torchrun --nproc_per_node=4 train.py\
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model resnet34 --output-dir resnet34 --weights ResNet34_Weights.IMAGENET1K_V1\
+    --batch-size 96 --epochs 10 --lr 0.0004 --lr-step-size 2 --lr-gamma 0.5\
+    --lr-warmup-method constant --lr-warmup-epochs 1 --lr-warmup-decay 0.\
+    --apply-trp --trp-depths 2 2 2 --trp-planes 256 --trp-lambdas 0.4 0.2 0.1 --print-freq 100
+
+
+# ✅ Test:  Acc@1 76.896 Acc@5 93.136
+torchrun --nproc_per_node=4 train.py\
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model resnet50 --output-dir resnet50 --weights ResNet50_Weights.IMAGENET1K_V1\
+    --batch-size 64 --epochs 10 --lr 0.0004 --lr-step-size 2 --lr-gamma 0.5\
+    --lr-warmup-method constant --lr-warmup-epochs 1 --lr-warmup-decay 0.\
+    --apply-trp --trp-depths 1 1 1 --trp-planes 1024 --trp-lambdas 0.4 0.2 0.1 --print-freq 100
 ```
 
 ## Evaluation
@@ -412,10 +429,15 @@ For Network Architecture Search,  please run the following command to evaluate o
 
 cd neural-architecture-search
 
-# Required: Download our ResNet-18 weights to /path/to/neural-architecture-search/resnet18
+# Required: Download our ResNet-34 weights to /path/to/neural-architecture-search/resnet34
 torchrun --nproc_per_node=4 train.py\
-  --data-path /path/to/imagenet/\
-  --model resnet18  --resume resnet18/model_8.pth --test-only
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model resnet34  --resume resnet34/model_8.pth --test-only
+
+# Required: Download our ResNet-50 weights to /path/to/neural-architecture-search/resnet50
+torchrun --nproc_per_node=4 train.py\
+    --data-path /home/cs/Documents/datasets/imagenet\
+    --model resnet50  --resume resnet50/model_9.pth --test-only
 ```