Instructions to use ashduino101/furry-species-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashduino101/furry-species-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ashduino101/furry-species-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ashduino101/furry-species-classification") model = AutoModelForImageClassification.from_pretrained("ashduino101/furry-species-classification") - Notebooks
- Google Colab
- Kaggle
Species Classification in Furry Art
This model was trained on ~16k images (filtered from ~40k). I may train it on a larger dataset in the future.
The model is capable of classifying about 86 species, including a few more common Pokemon, as well as some mythological and fictional species. The results may differ depending on the species, though the model is quite good with common ones such as foxes and wolves.
How to Use
Example using Pipelines (recommended):
from transformers import pipeline
classify = pipeline('image-classification', model='ashduino101/furry-species-classification')
scores = classify('your-image.png')
# Do something with the scores
Example using AutoModelForImageClassification:
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
image_processor = AutoImageProcessor.from_pretrained('ashduino101/furry-species-classification')
inputs = image_processor(Image.open('your-image.png'), return_tensors='pt')
model = AutoModelForImageClassification.from_pretrained("ashduino101/furry-species-classification")
with torch.no_grad():
result = model(**inputs)
# Do something with the result
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