
# 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):**
```py
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:**
```py
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
```


{"license": "mit", "tags": ["image-classification", "pytorch", "furry"]}
transformers pytorch safetensors vit image-classification furry license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #vit #image-classification #furry #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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):



Example using AutoModelForImageClassification:



# 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):



Example using AutoModelForImageClassification:
TAGS
#transformers #pytorch #safetensors #vit #image-classification #furry #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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):



Example using AutoModelForImageClassification:
TAGS
#transformers #pytorch #safetensors #vit #image-classification #furry #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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):



Example using AutoModelForImageClassification: