

# Model card

## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image. 

## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image. 

## Training and evaluation data
Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle [BIRDS 400 - SPECIES IMAGE CLASSIFICATION](https://www.kaggle.com/datasets/gpiosenka/100-bird-species). Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images. 

{"tags": ["fastai"]}
fastai region:us
TAGS
#fastai #region-us 



# Model card

## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image. 

## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image. 

## Training and evaluation data
Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle BIRDS 400 - SPECIES IMAGE CLASSIFICATION. Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images. 

# Model card ## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image. ## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image. ## Training and evaluation data
Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle BIRDS 400 - SPECIES IMAGE CLASSIFICATION. Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images.
TAGS
#fastai #region-us 
 # Model card ## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image. ## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image. ## Training and evaluation data
Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle BIRDS 400 - SPECIES IMAGE CLASSIFICATION. Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images.
passage: TAGS
#fastai #region-us 
# Model card## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image.## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image.## Training and evaluation data
Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle BIRDS 400 - SPECIES IMAGE CLASSIFICATION. Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images.