Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - deepfake-detection
+  - image-classification
+  - efficientnetv2
+  - pytorch
+  - timm
+  - huggingface
+  - colab
+---
+
+# Deepfake Image Detector
+
+Model repository: https://huggingface.co/Arko007/deepfake-image-detector
+
+This repository contains a pretrained deepfake image detector intended for research and experimentation. The model was provided as a PyTorch checkpoint and a small Colab-friendly inference script. The original training code and full training logs are not available; the best available evaluation result from the retained artifacts is included below.
+
+## Model Summary
+
+- Model name: Deepfake Image Detector (EfficientNetV2-S backbone)
+- HF repo id: Arko007/deepfake-image-detector
+- Framework: PyTorch (+ timm)
+- Backbone: tf_efficientnetv2_s (timm)
+- Input size: 380 × 380 RGB
+- Output: single logit for binary fake/real classification (sigmoid applied at inference)
+- Checkpoint file: pytorch_model.bin
+- Config file: config.json
+- License: Apache-2.0
+
+## Reported Evaluation (available artifact)
+
+- Reported snapshot evaluation: Epoch 6 | AUC: 0.9986
+- Note: This is the only retained per-epoch metric available from the artifacts. Full training logs, dataset split details, and other epoch metrics were not provided and appear to be lost.
+
+## Intended Use
+
+This model predicts whether an input face/image is likely a manipulated/deepfake (label = FAKE) or a real image (label = REAL). Intended uses include:
+
+- Research and benchmarking of image-level deepfake detection.
+- Prototyping content-moderation pipelines (with human-in-the-loop review).
+
+Not intended for:
+- High-stakes automated decisions without human review.
+- Medical, legal, or forensic conclusions without expert validation.
+
+## Limitations & Known Issues
+
+- Training code and dataset provenance are not available in this repository. Use caution when interpreting metrics and generalization claims.
+- The model was trained on a dataset that is not provided here; distributional shift to other datasets, domains, or manipulated types may significantly degrade performance.
+- The only retained evaluation metric is Epoch 6 AUC = 0.9986; other performance numbers, class breakdowns, and training curves are not available.
+- Model assumes centered face or face-like crop. Performance may drop on full-frame images or unseen camera artifacts.
+
+## Model Files
+
+- pytorch_model.bin — PyTorch checkpoint (state_dict)
+- config.json — Minimal config used by the inference script (contains model_name, epoch, and other metadata)
+- deepfake_detector.py — Colab-ready inference script (example usage)
+- README.md — This model card
+
+## Example config.json (expected fields)
+A minimal example of the config.json that pairs with pytorch_model.bin:
+
+{
+  "model_name": "tf_efficientnetv2_s",
+  "image_size": 380,
+  "epoch": 6,
+  "notes": "Only a single evaluation snapshot remains. Training artifacts incomplete."
+}
+
+## Inference (Colab / local)
+
+The repository includes a simple Colab-ready script deepfake_detector.py which implements:
+- Download of checkpoint and config from the HF hub
+- Initialization of a timm EfficientNetV2-S backbone with a custom classifier head
+- Image preprocessing via albumentations (Resize → Normalize → ToTensorV2)
+- Sigmoid on the model logit to produce a probability of "fake"
+
+High-level steps to run inference:
+
+1. Install dependencies (example):
+   pip install torch torchvision timm albumentations pillow huggingface-hub matplotlib
+
+2. Download the model files from the Hub (the script calls huggingface_hub.hf_hub_download).
+
+3. Run the included script in Colab or locally:
+   - Upload an image (PNG/JPG)
+   - The script resizes to 380×380, normalizes, and runs the model.
+   - Output: probability (0..1) of the image being FAKE. Threshold 0.5 by default.
+
+Example minimal inference snippet (matches the repository script):
+
+```python
+import torch
+import cv2
+from PIL import Image
+from deepfake_detector import DeepfakeDetector, TestConfig, get_inference_transform, load_model, predict_deepfake
+
+# load model (downloads checkpoint via hf_hub_download inside)
+model = load_model()  # returns a model already set to eval on TestConfig.DEVICE
+transform = get_inference_transform(TestConfig.IMAGE_SIZE)
+
+is_fake, prob, confidence = predict_deepfake(model, "example.jpg", transform)
+print(f"Prediction: {'FAKE' if is_fake else 'REAL'}")
+print(f"Probability (fake): {prob:.4f}, Confidence: {confidence:.4f}")
+```
+
+## Reproducibility Notes
+
+- The repository does not contain the original training script or dataset. The inference script is intended for running the provided checkpoint only.
+- If you require retraining or further experiments, replace the placeholder classifier with the same architecture used in training and retrain using your own dataset, ensuring appropriate splits and class balance.
+
+## Responsible Use & Ethics
+
+Deepfake detection models have social impact. Use responsibly:
+- Disclose limitations to stakeholders.
+- Avoid over-reliance on model outputs for high-stakes decisions.
+- Combine with manual verification and multi-modal signals when possible.
+
+## Contact / Attribution
+
+Model owner: Arko007  
+HF model page: https://huggingface.co/Arko007/deepfake-image-detector
+
+If you have additional training artifacts (logs, dataset details, training script), please add them to the repository to improve reproducibility and transparency.