Single-Label-General-Image-Classifier

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Quick Summary

This repository contains a Vision Transformer (ViT)-based AI model fine-tuned for image classification on the CIFAR-100 dataset.

Code Examples

🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))
🔍 Inference Examplepythonpytorch
from PIL import Image
import torch

def predict(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()
    return dataset["train"].features["fine_label"].int2str(predicted_class)

print(predict("sample_image.jpg"))

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