Deepfake-Detection-Exp-02-21

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license:apache-2.0

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prithivMLmods

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Quick Summary

Deepfake-Detection-Exp-02-21 is a minimalist, high-quality dataset trained on a ViT-based model for image classification, distinguishing between deepfake and real images.

Code Examples

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

Predict on an imagepythontransformers

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")

# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")

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