
## Model description

**Upside down detector**: Model to detect if images are upside down

* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not.

## Intended uses & limitations
Intended to showcase skill set of being able to train a simple CNN classifier.

## How to use
n/a

## Limitations and bias
Trained on a relatively small dataset, hence it's hard to derive conclusions. 

## Training data
cifar10

## Training procedure
Trained using Keras with Nadam classifier with ReduceLROnPlateau which halves the learning rate when the validation loss doesn't improve for 5 iterations

## Evaluation results
The classifier was able to achieve 90% validation accuracy
{"language": "en", "license": "mit", "tags": "image-classification", "metrics": "accuracy (https://hf.co/metrics/accuracy)", "dataset": "cifar10"}
image-classification en license:mit region:us
TAGS
#image-classification #en #license-mit #region-us 


## Model description

Upside down detector: Model to detect if images are upside down

* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not.

## Intended uses & limitations
Intended to showcase skill set of being able to train a simple CNN classifier.

## How to use
n/a

## Limitations and bias
Trained on a relatively small dataset, hence it's hard to derive conclusions. 

## Training data
cifar10

## Training procedure
Trained using Keras with Nadam classifier with ReduceLROnPlateau which halves the learning rate when the validation loss doesn't improve for 5 iterations

## Evaluation results
The classifier was able to achieve 90% validation accuracy
## Model description

Upside down detector: Model to detect if images are upside down

* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not. ## Intended uses & limitations
Intended to showcase skill set of being able to train a simple CNN classifier. ## How to use
n/a ## Limitations and bias
Trained on a relatively small dataset, hence it's hard to derive conclusions. ## Training data
cifar10 ## Training procedure
Trained using Keras with Nadam classifier with ReduceLROnPlateau which halves the learning rate when the validation loss doesn't improve for 5 iterations ## Evaluation results
The classifier was able to achieve 90% validation accuracy
TAGS
#image-classification #en #license-mit #region-us 
 ## Model description

Upside down detector: Model to detect if images are upside down

* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not. ## Intended uses & limitations
Intended to showcase skill set of being able to train a simple CNN classifier. ## How to use
n/a ## Limitations and bias
Trained on a relatively small dataset, hence it's hard to derive conclusions. ## Training data
cifar10 ## Training procedure
Trained using Keras with Nadam classifier with ReduceLROnPlateau which halves the learning rate when the validation loss doesn't improve for 5 iterations ## Evaluation results
The classifier was able to achieve 90% validation accuracy
passage: TAGS
#image-classification #en #license-mit #region-us 
## Model description

Upside down detector: Model to detect if images are upside down

* Picked a dataset of natural images - cifar10
* Synthetically turned some of images upside down. Created a training and test set.
* Trained it to classify image orientation ie if the image is upside down or not.## Intended uses & limitations
Intended to showcase skill set of being able to train a simple CNN classifier.## How to use
n/a## Limitations and bias
Trained on a relatively small dataset, hence it's hard to derive conclusions.## Training data
cifar10## Training procedure
Trained using Keras with Nadam classifier with ReduceLROnPlateau which halves the learning rate when the validation loss doesn't improve for 5 iterations## Evaluation results
The classifier was able to achieve 90% validation accuracy