ModelListedExternal

Convnextv2 Real Fake

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Sourcehuggingface.co/computervisionpro/convnextv2-real-fake/tree/2e84d97cd9d1dd6f8738a6602251108d8a659434

KindOtherBase modelfacebook/convnextv2-tiny-1k-224VersionRevision 2e84d97

Model source

Kind: Other
File size: 106 MB
Base model: facebook/convnextv2-tiny-1k-224
Version: vRevision 2e84d97
Updated: Sep 17, 2026
Downloads: 48
Source: Hugging Face

Source description

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Model Sources [optional]

Repository: github
Paper: ConvNeXt V2

Model Description

The model is fine-tuned on ConvNext V2 model.

Uses

This finetuned model can be used for text classification. It has been trained to classify real and fake images.

Hardware

Hardware Type: GPU T4
Hours used: ~ 14 Minutes
Cloud Provider: Kaggle

Inference code


import os
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification


MODEL_ID = "computervisionpro/convnextv2-real-fake"


def predict(image_path, model_id=MODEL_ID):
    # device = "cuda" if torch.cuda.is_available() else "cpu"
    device = "cpu"
    # hf_token = os.getenv("HF_TOKEN") or None

    processor = AutoImageProcessor.from_pretrained(model_id)
    model = AutoModelForImageClassification.from_pretrained(model_id)
    model.to(device)
    model.eval()

    image = Image.open(image_path).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    inputs = {key: value.to(device) for key, value in inputs.items()}

    with torch.inference_mode():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=-1)[0]

    pred_id = int(torch.argmax(probs).item())
    label = model.config.id2label.get(pred_id, str(pred_id))
    confidence = float(probs[pred_id].item())

    return {
        "image": image_path,
        "model": model_id,
        "prediction": label,
        "confidence": confidence,
        "probabilities": {
            model.config.id2label.get(i, str(i)): float(prob.item())
            for i, prob in enumerate(probs)
        },
    }


result = predict("./dataset/test/fake/fake_1006.jpg")
print()
print(result)

Results

WandB

Important Links

Sources

1 source

Hugging FacePrimaryVersion 2e84d97cd9d1dd6f8738a6602251108d8a659434

huggingface.co/computervisionpro/convnextv2-real-fake/tree/2e84d97cd9d1dd6f8738a6602251108d8a659434

Verified Sep 17

Model artifacts

1 artifact

model.safetensors

safetensors · 106 MB · SHA-256 652a5fc112b1…fccf · Hugging Face

Download

Source excerpts

2 excerpts

ReadmeSep 17huggingface.co/computervisionpro/convnextv2-real-fake

--- library_name: transformers tags: - classification - deepfake base_model: - facebook/convnextv2-tiny-1k-224 --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. ### Model Sources [optional]  - **Repository:** [github](https://github.com/g25ait2119/mlops-pipeline-group30) - **Paper:** [ConvNeXt V2](https://arxiv.org/abs/2301.00808) ## Model Description The model is fine-tuned on ConvNext V2 model. ## Uses This finetuned model can be used for text classification. It has been trained to classify real and fake images. #### Hardware - **Hardware Type:** GPU T4 - **Hours used:** ~ 14 Minutes - **Cloud Provider:** Kaggle ### Inference code ```python import os import torch from PIL import Image from transformers import AutoImageProcessor, AutoModelForImageClassification MODEL_ID = "computervisionpro/convnextv2-real-fake" def predict(image_path, model_id=MODEL_ID): # device = "cuda" if torch.cuda.is_available() else "cpu" device = "cpu" # hf_token = os.getenv("HF_TOKEN") or None processor = AutoImageProcessor.from_pretrained(model_id) model = AutoModelForImageClassification.from_pretrained(model_id) model.to(device) model.eval() image = Image.open(image_path).convert("RGB") inputs = processor(images=image, return_tensors="pt") inputs = {key: value.to(device) for key, value in inputs.items()} with torch.inference_mode(): outputs = model(**inputs) probs = torch.softmax(outputs.logits, dim=-1)[0] pred_id = int(torch.argmax(probs).item()) label = model.config.id2label.get(pred_id, str(pred_id)) confidence = float(probs[pred_id].item()) return { "image": image_path, "model": model_id, "prediction": label, "confidence": confidence, "probabilities": { model.config.id2label.get(i, str(i)): float(prob.item()) for i, prob in enumerate(probs) }, } result = predict("./dataset/test/fake/fake_1006.jpg") print() print(result) ``` ### Results - [WandB](https://wandb.ai/computervisionpro-na/mlops-assignment3) ## Important Links - [Data](https://www.kaggle.com/datasets/manjilkarki/deepfake-and-real-images) - [Kaggle...

NoteSep 17huggingface.co/computervisionpro/convnextv2-real-fake

Source context: 48 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo computervisionpro/convnextv2-real-fake