
## General Information:
Used Dataset: cats_vs_dogs (https://huggingface.co/datasets/cats_vs_dogs)

Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. 

Used Library: Pytorch

Used Model: ResNet18 from torchvision

Number of classes: 2 (0 means No flip and 1 means Flipped image)

Train Test Split: 70-30

## Some sample Images and Labels from created dataset
![Alt sample image png](sample.png)


## Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted. 

-  ./kagglecatsanddogs_3367a/PetImages/Cat/666.jpg
-  ./kagglecatsanddogs_3367a/PetImages/Cat/10404.jpg
-  ./kagglecatsanddogs_3367a/PetImages/Dog/11702.jpg

## Training Information:

Total Epoch: 5

Pretrained: True (ImageNet weight) (Every layer is trainable)

Image Size: 224 x 224

Batch Size: 128

Optimizer: SGD

Learning Rate: 0.001 (Constant throughout the training)

Momentum: 0.9

Loss: CrossEntropy Loss


## Result:
Accuracy: 98.4266

F1: 98.4271

Recall: 98.4261

Precision: 98.4265

## Confusion Matrix:
![Alt confusion matrix png](cm.png)

## Some Misclassified Images (Randomly Selected):
![Alt misclassified image png](misclassified.png)

## Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For example, instead of using a constant learning rate, we could try a cyclical learning rate. This type of learning rate helps the model overcome local minima. 
- If we consider the rightmost image in the above figure, we see that the pose of the cat is different than most of the images in the training set. I think Augmentation like CutMix will be helpful in this scenario. 
{"license": "apache-2.0"}
license:apache-2.0 region:us
TAGS
#license-apache-2.0 #region-us 


## General Information:
Used Dataset: cats_vs_dogs (URL

Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. 

Used Library: Pytorch

Used Model: ResNet18 from torchvision

Number of classes: 2 (0 means No flip and 1 means Flipped image)

Train Test Split: 70-30

## Some sample Images and Labels from created dataset
!Alt sample image png


## Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted. 

-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Dog/URL

## Training Information:

Total Epoch: 5

Pretrained: True (ImageNet weight) (Every layer is trainable)

Image Size: 224 x 224

Batch Size: 128

Optimizer: SGD

Learning Rate: 0.001 (Constant throughout the training)

Momentum: 0.9

Loss: CrossEntropy Loss


## Result:
Accuracy: 98.4266

F1: 98.4271

Recall: 98.4261

Precision: 98.4265

## Confusion Matrix:
!Alt confusion matrix png

## Some Misclassified Images (Randomly Selected):
!Alt misclassified image png

## Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For example, instead of using a constant learning rate, we could try a cyclical learning rate. This type of learning rate helps the model overcome local minima. 
- If we consider the rightmost image in the above figure, we see that the pose of the cat is different than most of the images in the training set. I think Augmentation like CutMix will be helpful in this scenario. 
## General Information:
Used Dataset: cats_vs_dogs (URL

Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. 

Used Library: Pytorch

Used Model: ResNet18 from torchvision

Number of classes: 2 (0 means No flip and 1 means Flipped image)

Train Test Split: 70-30 ## Some sample Images and Labels from created dataset
!Alt sample image png ## Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted. 

-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Dog/URL ## Training Information:

Total Epoch: 5

Pretrained: True (ImageNet weight) (Every layer is trainable)

Image Size: 224 x 224

Batch Size: 128

Optimizer: SGD

Learning Rate: 0.001 (Constant throughout the training)

Momentum: 0.9

Loss: CrossEntropy Loss ## Result:
Accuracy: 98.4266

F1: 98.4271

Recall: 98.4261

Precision: 98.4265 ## Confusion Matrix:
!Alt confusion matrix png ## Some Misclassified Images (Randomly Selected):
!Alt misclassified image png ## Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For example, instead of using a constant learning rate, we could try a cyclical learning rate. This type of learning rate helps the model overcome local minima. 
- If we consider the rightmost image in the above figure, we see that the pose of the cat is different than most of the images in the training set. I think Augmentation like CutMix will be helpful in this scenario.
TAGS
#license-apache-2.0 #region-us 
 ## General Information:
Used Dataset: cats_vs_dogs (URL

Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. 

Used Library: Pytorch

Used Model: ResNet18 from torchvision

Number of classes: 2 (0 means No flip and 1 means Flipped image)

Train Test Split: 70-30 ## Some sample Images and Labels from created dataset
!Alt sample image png ## Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted. 

-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Dog/URL ## Training Information:

Total Epoch: 5

Pretrained: True (ImageNet weight) (Every layer is trainable)

Image Size: 224 x 224

Batch Size: 128

Optimizer: SGD

Learning Rate: 0.001 (Constant throughout the training)

Momentum: 0.9

Loss: CrossEntropy Loss ## Result:
Accuracy: 98.4266

F1: 98.4271

Recall: 98.4261

Precision: 98.4265 ## Confusion Matrix:
!Alt confusion matrix png ## Some Misclassified Images (Randomly Selected):
!Alt misclassified image png ## Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For example, instead of using a constant learning rate, we could try a cyclical learning rate. This type of learning rate helps the model overcome local minima. 
- If we consider the rightmost image in the above figure, we see that the pose of the cat is different than most of the images in the training set. I think Augmentation like CutMix will be helpful in this scenario.
passage: TAGS
#license-apache-2.0 #region-us 
## General Information:
Used Dataset: cats_vs_dogs (URL

Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0. 

Used Library: Pytorch

Used Model: ResNet18 from torchvision

Number of classes: 2 (0 means No flip and 1 means Flipped image)

Train Test Split: 70-30## Some sample Images and Labels from created dataset
!Alt sample image png## Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted. 

-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Cat/URL
-  ./kagglecatsanddogs_3367a/PetImages/Dog/URL## Training Information:

Total Epoch: 5

Pretrained: True (ImageNet weight) (Every layer is trainable)

Image Size: 224 x 224

Batch Size: 128

Optimizer: SGD

Learning Rate: 0.001 (Constant throughout the training)

Momentum: 0.9

Loss: CrossEntropy Loss## Result:
Accuracy: 98.4266

F1: 98.4271

Recall: 98.4261

Precision: 98.4265## Confusion Matrix:
!Alt confusion matrix png## Some Misclassified Images (Randomly Selected):
!Alt misclassified image png## Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For example, instead of using a constant learning rate, we could try a cyclical learning rate. This type of learning rate helps the model overcome local minima. 
- If we consider the rightmost image in the above figure, we see that the pose of the cat is different than most of the images in the training set. I think Augmentation like CutMix will be helpful in this scenario.