Instructions to use edwinhung/bird_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastai
How to use edwinhung/bird_classifier with fastai:
from huggingface_hub import from_pretrained_fastai learn = from_pretrained_fastai("edwinhung/bird_classifier") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - fastai | |
| # 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. | |