How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="lrzjason/noise-classifier")
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("lrzjason/noise-classifier")
model = AutoModelForImageClassification.from_pretrained("lrzjason/noise-classifier", device_map="auto")
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An vit classifier for handling noise image like this

0b36d3c4-da8d-4fb1-bc14-a948af35f02e.jpg

It has limitation inbetween clear and noise

from datasets import load_dataset
from PIL import Image
from transformers import ViTImageProcessor, ViTForImageClassification, TrainingArguments, Trainer
import torch
import numpy as np
from datasets import load_metric
import os
import shutil

model_name_or_path = 'lrzjason/noise-classifier'
image_processor = ViTImageProcessor.from_pretrained(model_name_or_path)
model = ViTForImageClassification.from_pretrained(model_name_or_path)

input_dir = ''
file = 'b5b457f4-5b52-4d68-be1b-9a2f557465f6.jpg'
image = Image.open(os.path.join(input_dir, file))

inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
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12
Safetensors
Model size
85.8M params
Tensor type
F32
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