Instructions to use MohamedFiyaz/bird-species-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MohamedFiyaz/bird-species-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MohamedFiyaz/bird-species-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("MohamedFiyaz/bird-species-classifier") model = AutoModelForImageClassification.from_pretrained("MohamedFiyaz/bird-species-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Bird Species Classifier (ViT)
Fine-tuned Vision Transformer (ViT-base-patch16-224) on the CUB-200-2011 dataset for 200-class bird species classification.
Model Details
- Architecture: ViT-base-patch16-224 (Google)
- Dataset: CUB-200-2011 (11,788 images, 200 species)
- Training: Transfer learning with AdamW optimizer, 5 epochs
- Test Accuracy: 99.93%
- Framework: PyTorch + HuggingFace Transformers
Author
Mohamed Fiyaz
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Model tree for MohamedFiyaz/bird-species-classifier
Base model
google/vit-base-patch16-224