metadata
license: apache-2.0
pipeline_tag: image-classification
tags:
- pytorch
- vision
library_name: transformers
This model is the product of curiosity—imagine a choice that allows you to label anime images!
Disclaimer: The model has been trained on an entirely new dataset. Predictions made by the model prior to 2023 might be off. It's advisable to fine-tune the model according to your specific use case.
Quick setup guide:
from transformers.modeling_outputs import ImageClassifierOutput
from transformers import ViTImageProcessor, ViTForImageClassification
import torch
from PIL import Image
model_name_or_path = "Ojimi/vit-anime-caption"
processor = ViTImageProcessor.from_pretrained(model_name_or_path)
model = ViTForImageClassification.from_pretrained(model_name_or_path)
threshold = 0.3
device = torch.device('cuda')
image = Image.open(YOUR_IMAGE_PATH)
inputs = processor(image, return_tensors='pt')
model.to(device=device)
model.eval()
with torch.no_grad():
pixel_values = inputs['pixel_values'].to(device=device)
outputs : ImageClassifierOutput = model(pixel_values=pixel_values)
logits = outputs.logits # The raw scores before applying any activation
sigmoid = torch.nn.Sigmoid() # Sigmoid function to convert logits to probabilities
logits : torch.FloatTensor = sigmoid(logits) # Applying sigmoid activation
predictions = [] # List to store predictions
for idx, p in enumerate(logits[0]):
if p > threshold: # Applying a threshold of 0.3 to consider a class prediction
predictions.append((model.config.id2label[idx], p.item())) # Storing class label and probability
for tag in predictions:
print(tag)
Why the Sigmoid?
- Sigmoid turns boring scores into fun probabilities, so you can use thresholds and find more cool tags.
- It's like a wizard turning regular stuff into magic potions!