From 833fae5c5948d7ab677a2bfba8d9fd57fa7ee482 Mon Sep 17 00:00:00 2001 From: coderx7 Date: Thu, 4 May 2023 13:23:38 +0330 Subject: [PATCH 1/3] make a method private in simplenetmodel and update readme made `remove_network_settings` method private. removed the generic simplenet entry from hubconfig.py updated readme to reflect the latest changes including: 1.addition of simplenet to pytorch hub (aka torch hub) 2.addition of a how to use section including link to a huggingface demo and a code snippet showing how to use simplenet in torch hub --- README.md | 69 ++++++++++++++++++- cifar/models/simplenet.py | 26 +++---- hubconf.py | 1 - imagenet/simplenet.py | 26 +++---- .../timm/models/simplenet.py | 18 ++--- 5 files changed, 103 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index ea03ed9..f75b235 100644 --- a/README.md +++ b/README.md @@ -18,8 +18,10 @@ The pytorch implementation is also very effieicent and the whole model takes onl #### Update History: 
+-- 2023 May 13:
+  -- Add support for torch hub.
 -- 2023 Apr 14:
-  -- update benchmark results
+  -- update benchmark results.
 -- 2023 Apr 13:
   -- new weights for the removed paddings for 1x1 conv layers.
   -- some minor fixes
@@ -147,6 +149,71 @@ For all benchmark results [look here](https://github.com/Coderx7/SimpleNet_Pytor
 #### Models and logs  
 -- refer to each dataset directory in the repository for further information on how to access models.
 
+### How to use
+You can either download the [simplenet.py model](./imagenet/simplenet.py) and use it directly in your projects 
+or you can use torch hub.  
+
+Using torch hub you can do something like this:  
+```python
+import torch
+# use the latest master
+model = torch.hub.load("coderx7/simplenet_pytorch", "simplenetv1_5m_m1", pretrained=True)
+# or any of these variants at the moment
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_5m_m2", pretrained=True)
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_9m_m1", pretrained=True)
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_9m_m2", pretrained=True)
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m1_05", pretrained=True)
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m2_05", pretrained=True)
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m1_075", pretrained=True)
+# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m2_075", pretrained=True)
+model.eval()
+
+# Download an example image from the pytorch website
+import urllib
+url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# sample execution (requires torchvision)
+from PIL import Image
+from torchvision import transforms
+input_image = Image.open(filename)
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+input_tensor = preprocess(input_image)
+input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+# move the input and model to GPU for speed if available
+if torch.cuda.is_available():
+    input_batch = input_batch.to('cuda')
+    model.to('cuda')
+
+with torch.no_grad():
+    output = model(input_batch)
+# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
+print(output[0])
+# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+probabilities = torch.nn.functional.softmax(output[0], dim=0)
+print(probabilities)
+
+# Download ImageNet labels
+!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
+
+# Read the categories
+with open("imagenet_classes.txt", "r") as f:
+    categories = [s.strip() for s in f.readlines()]
+# Show top categories per image
+top5_prob, top5_catid = torch.topk(probabilities, 5)
+for i in range(top5_prob.size(0)):
+    print(categories[top5_catid[i]], top5_prob[i].item())
+```
+
+or you can run the demo online from huggingface simplenetspace: https://huggingface.co/spaces/coderx7/simplenet 
+
 
 ## Citation
 If you find SimpleNet useful in your research, please consider citing:
diff --git a/cifar/models/simplenet.py b/cifar/models/simplenet.py
index 9d9acbf..5ed484a 100644
--- a/cifar/models/simplenet.py
+++ b/cifar/models/simplenet.py
@@ -404,7 +404,7 @@ def simplenet(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     return _gen_simplenet(model_variant, num_classes, in_chans, scale, network_idx, mode, pretrained, drop_rates)
 
 
-def remove_network_settings(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+def _remove_network_settings(kwargs: Dict[str, Any]) -> Dict[str, Any]:
     """Removes network related settings passed in kwargs for predefined network configruations below
 
     Returns:
@@ -420,7 +420,7 @@ def simplenet_cifar_310k(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     that were used in the paper 
     """
     model_variant = "simplenet_cifar_310k"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=2, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -429,7 +429,7 @@ def simplenet_cifar_460k(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     that were used in the paper 
     """
     model_variant = "simplenet_cifar_460k"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=3, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -437,7 +437,7 @@ def simplenet_cifar_5m(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     """The original implementation of simplenet trained on cifar10/100 in caffe.
     """
     model_variant = "simplenet_cifar_5m"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=4, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -447,7 +447,7 @@ def simplenet_cifar_5m_extra_pool(pretrained: bool = False, **kwargs: Any) -> Si
     this is just here to be able to load the weights that were trained using this variation still available on the repository. 
     """
     model_variant = "simplenet_cifar_5m_extra_pool"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=5, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -463,7 +463,7 @@ def simplenetv1_small_m1_05(pretrained: bool = False, **kwargs: Any) -> SimpleNe
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m1_05"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.5, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -478,7 +478,7 @@ def simplenetv1_small_m2_05(pretrained: bool = False, **kwargs: Any) -> SimpleNe
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m2_05"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.5, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -493,7 +493,7 @@ def simplenetv1_small_m1_075(pretrained: bool = False, **kwargs: Any) -> SimpleN
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m1_075"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.75, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -508,7 +508,7 @@ def simplenetv1_small_m2_075(pretrained: bool = False, **kwargs: Any) -> SimpleN
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m2_075"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.75, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -523,7 +523,7 @@ def simplenetv1_5m_m1(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_5m_m1"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -538,7 +538,7 @@ def simplenetv1_5m_m2(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_5m_m2"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -553,7 +553,7 @@ def simplenetv1_9m_m1(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_9m_m1"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=1, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -568,7 +568,7 @@ def simplenetv1_9m_m2(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_9m_m2"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=1, mode=2, pretrained=pretrained, **model_args)
 
 
diff --git a/hubconf.py b/hubconf.py
index 0c83215..ac5d31f 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -4,7 +4,6 @@
 from torchvision.models import get_model_weights, get_weight
 
 from imagenet.simplenet import (
-    simplenet,
     simplenetv1_5m_m1,
     simplenetv1_5m_m2,
     simplenetv1_9m_m1,
diff --git a/imagenet/simplenet.py b/imagenet/simplenet.py
index 01dcf0b..e104792 100644
--- a/imagenet/simplenet.py
+++ b/imagenet/simplenet.py
@@ -405,7 +405,7 @@ def simplenet(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     return _gen_simplenet(model_variant, num_classes, in_chans, scale, network_idx, mode, pretrained, drop_rates)
 
 
-def remove_network_settings(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+def _remove_network_settings(kwargs: Dict[str, Any]) -> Dict[str, Any]:
     """Removes network related settings passed in kwargs for predefined network configruations below
 
     Returns:
@@ -421,7 +421,7 @@ def simplenet_cifar_310k(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     that were used in the paper 
     """
     model_variant = "simplenet_cifar_310k"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=2, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -430,7 +430,7 @@ def simplenet_cifar_460k(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     that were used in the paper 
     """
     model_variant = "simplenet_cifar_460k"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=3, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -438,7 +438,7 @@ def simplenet_cifar_5m(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     """The original implementation of simplenet trained on cifar10/100 in caffe.
     """
     model_variant = "simplenet_cifar_5m"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=4, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -448,7 +448,7 @@ def simplenet_cifar_5m_extra_pool(pretrained: bool = False, **kwargs: Any) -> Si
     this is just here to be able to load the weights that were trained using this variation still available on the repository. 
     """
     model_variant = "simplenet_cifar_5m_extra_pool"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, network_idx=5, mode=0, pretrained=pretrained, **model_args)
 
 
@@ -464,7 +464,7 @@ def simplenetv1_small_m1_05(pretrained: bool = False, **kwargs: Any) -> SimpleNe
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m1_05"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.5, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -479,7 +479,7 @@ def simplenetv1_small_m2_05(pretrained: bool = False, **kwargs: Any) -> SimpleNe
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m2_05"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.5, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -494,7 +494,7 @@ def simplenetv1_small_m1_075(pretrained: bool = False, **kwargs: Any) -> SimpleN
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m1_075"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.75, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -509,7 +509,7 @@ def simplenetv1_small_m2_075(pretrained: bool = False, **kwargs: Any) -> SimpleN
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m2_075"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.75, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -524,7 +524,7 @@ def simplenetv1_5m_m1(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_5m_m1"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -539,7 +539,7 @@ def simplenetv1_5m_m2(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_5m_m2"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -554,7 +554,7 @@ def simplenetv1_9m_m1(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_9m_m1"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=1, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -569,7 +569,7 @@ def simplenetv1_9m_m2(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_9m_m2"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=1, mode=2, pretrained=pretrained, **model_args)
 
 
diff --git a/imagenet/training_scripts/imagenet_training/timm/models/simplenet.py b/imagenet/training_scripts/imagenet_training/timm/models/simplenet.py
index 52ae526..e54a79e 100644
--- a/imagenet/training_scripts/imagenet_training/timm/models/simplenet.py
+++ b/imagenet/training_scripts/imagenet_training/timm/models/simplenet.py
@@ -437,7 +437,7 @@ def simplenet(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
     return _gen_simplenet(model_variant, num_classes, in_chans, scale, network_idx, mode, pretrained, drop_rates)
 
 
-def remove_network_settings(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+def _remove_network_settings(kwargs: Dict[str, Any]) -> Dict[str, Any]:
     """Removes network related settings passed in kwargs for predefined network configruations below
 
     Returns:
@@ -497,7 +497,7 @@ def simplenetv1_small_m1_05(pretrained: bool = False, **kwargs: Any) -> SimpleNe
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m1_05"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.5, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -513,7 +513,7 @@ def simplenetv1_small_m2_05(pretrained: bool = False, **kwargs: Any) -> SimpleNe
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m2_05"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.5, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -529,7 +529,7 @@ def simplenetv1_small_m1_075(pretrained: bool = False, **kwargs: Any) -> SimpleN
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m1_075"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.75, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -545,7 +545,7 @@ def simplenetv1_small_m2_075(pretrained: bool = False, **kwargs: Any) -> SimpleN
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_small_m2_075"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=0.75, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -561,7 +561,7 @@ def simplenetv1_5m_m1(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_5m_m1"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=0, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -577,7 +577,7 @@ def simplenetv1_5m_m2(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_5m_m2"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=0, mode=2, pretrained=pretrained, **model_args)
 
 
@@ -593,7 +593,7 @@ def simplenetv1_9m_m1(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_9m_m1"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=1, mode=1, pretrained=pretrained, **model_args)
 
 
@@ -609,7 +609,7 @@ def simplenetv1_9m_m2(pretrained: bool = False, **kwargs: Any) -> SimpleNet:
         SimpleNet: a SimpleNet model instance is returned upon successful instantiation. 
     """
     model_variant = "simplenetv1_9m_m2"
-    model_args = remove_network_settings(kwargs)
+    model_args = _remove_network_settings(kwargs)
     return _gen_simplenet(model_variant, scale=1.0, network_idx=1, mode=2, pretrained=pretrained, **model_args)
 
 

From 237024326ac327394f8bc07575cfa770eb49d893 Mon Sep 17 00:00:00 2001
From: Seyyed Hossein Hasanpour 
Date: Wed, 23 Aug 2023 08:26:28 +0330
Subject: [PATCH 2/3] fix padding not defined in cifar10 models

---
 cifar/models/simplenet.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cifar/models/simplenet.py b/cifar/models/simplenet.py
index 5ed484a..b808219 100644
--- a/cifar/models/simplenet.py
+++ b/cifar/models/simplenet.py
@@ -297,6 +297,7 @@ def _make_layers(self, scale: float):
             # check to convert any possible integer value to its decimal counterpart.
             custom_dropout = None if custom_dropout is None else float(custom_dropout)
             kernel_size = 3
+            padding = 1
             if layer_type == ['k1']:
                 kernel_size = 1
                 padding = 0 

From 4334c57535bc0e8425a2c3b018fdc0fec548b954 Mon Sep 17 00:00:00 2001
From: Seyyed Hossein Hasanpour 
Date: Wed, 7 Feb 2024 22:11:21 +0330
Subject: [PATCH 3/3] Update CIFAR10 README.md to address the issue in loading
 old checkpoints

Added note on how to load old CIFAR10 weights using the refactored model.

Basically the 'module.' prefix pertaining to DataParallel wrapper needs to be removed (or the model needs to be wrapped in DataParallel), and more importantly, the extra dropout layers need to be removed except for the ones after maxpooling layers.
These are explained in the
---
 cifar/README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/cifar/README.md b/cifar/README.md
index 563a9e8..0649e1a 100644
--- a/cifar/README.md
+++ b/cifar/README.md
@@ -50,9 +50,8 @@ Simply initiate the training like :
 `python3 main.py ./data/cifar.python --dataset cifar10 --arch simplenet_cifar_5m --save_path ./snapshots/simplenet --epochs 540 --batch_size 100 --workers 2`
 
 
-Note 1: the initial learning rate, and optimization policy is hard coded just like caffe.  
-Note 2: for testing the cifar10/100 weights located in the repository, use the `simplenet_cifar_5m_extra_pool` model instead. see [issue #5](https://github.com/Coderx7/SimpleNet_Pytorch/issues/5) for more information. 
-
+Note 1: The initial learning rate, and optimization policy is hard coded just like caffe.  
+Note 2: For testing the cifar10/100 weights located in the repository, use the `simplenet_cifar_5m_extra_pool` model instead. see [issue #5](https://github.com/Coderx7/SimpleNet_Pytorch/issues/5) and [Error loading checkpoint#7](https://github.com/Coderx7/SimpleNet_Pytorch/issues/7#issuecomment-1932491193) for more information.
 
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