ModelPublicadoExterno

TIDAL

Tampered image detection and localization model built with a 9-channel multi-quality RGB ELA input and a UNet with a ResNet-34 encoder.

Fuentehuggingface.co/the-harsh-vardhan/TIDAL/tree/6bc4c3fd066f13d614949db64eb7f1abdc912ada

TipoOtherVersiónBest modelCcalidad

Fuente del modelo

Tipo: Other
Tamaño del archivo: 93,4–93,5 MB
Versión: vBest model
Actualizado: 1 nov 2026
Fuente: Hugging Face

Descripción de la fuente

TIDAL vR.P.19_U

Tampered image detection and localization model built with a 9-channel multi-quality RGB ELA input and a UNet with a ResNet-34 encoder.

Included files

checkpoints/best_model.pt
checkpoints/vR.P.19_U_unet_resnet34_mqela_rgb.pth
notebooks/vR.P.19_U Image Detection and Localisation.ipynb

Notes

Model version: vR.P.19_U
Primary task: image tampering localization with image-level tamper detection
Input representation: 9-channel multi-quality RGB ELA at Q=75/85/95

Minimal load example

import segmentation_models_pytorch as smp
import torch

model = smp.Unet(
    encoder_name="resnet34",
    encoder_weights=None,
    in_channels=9,
    classes=1,
    activation=None,
)

state = torch.load("checkpoints/best_model.pt", map_location="cpu", weights_only=False)
weights = state.get("model_state_dict", state) if isinstance(state, dict) else state
model.load_state_dict(weights, strict=False)
model.eval()

Fuentes

1 fuente

Hugging FacePrincipalVersión 6bc4c3fd066f13d614949db64eb7f1abdc912ada

huggingface.co/the-harsh-vardhan/TIDAL/tree/6bc4c3fd066f13d614949db64eb7f1abdc912ada

Verificado 1 nov

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Artefactos del modelo

3 artefactos

checkpoints/best_model.pt

pt · 93,5 MB · SHA-256 fea3ca8cc76c…fc7a · Hugging Face

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checkpoints/vR.P.19_U_unet_resnet34_mqela_rgb.pth

pth · 93,5 MB · SHA-256 fea3ca8cc76c…fc7a · Hugging Face

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checkpoints/vR.P.30.1/best_model.pt

pt · 93,4 MB · SHA-256 ca3aa144e6ca…e061 · Hugging Face

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Extractos de fuentes

2 extractos

Readme18 agohuggingface.co/the-harsh-vardhan/TIDAL

--- license: apache-2.0 library_name: pytorch tags: - image-segmentation - image-forensics - tamper-detection - localization - unet - resnet34 --- # TIDAL vR.P.19_U Tampered image detection and localization model built with a 9-channel multi-quality RGB ELA input and a UNet with a ResNet-34 encoder. ## Included files - `checkpoints/best_model.pt` - `checkpoints/vR.P.19_U_unet_resnet34_mqela_rgb.pth` - `notebooks/vR.P.19_U Image Detection and Localisation.ipynb` ## Notes - Model version: `vR.P.19_U` - Primary task: image tampering localization with image-level tamper detection - Input representation: 9-channel multi-quality RGB ELA at Q=75/85/95 ## Minimal load example ```python import segmentation_models_pytorch as smp import torch model = smp.Unet( encoder_name="resnet34", encoder_weights=None, in_channels=9, classes=1, activation=None, ) state = torch.load("checkpoints/best_model.pt", map_location="cpu", weights_only=False) weights = state.get("model_state_dict", state) if isinstance(state, dict) else state model.load_state_dict(weights, strict=False) model.eval() ```

Note18 agohuggingface.co/the-harsh-vardhan/TIDAL

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo the-harsh-vardhan/TIDAL