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ash12321/seedream-4.5-specialist-h100

sourceHugging Facemitupdated 9mo agoView on Hugging Face
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Model Card

SeeDream 4.5 Specialist ๐ŸŽฏ

Specialist detector for SeeDream 4.5 (unfrozen last layer, 97-99% accuracy)

H100-Optimized Specialist Detector with Unfrozen Last Layer

Architecture

  • โ€”Base: Vision Transformer (ViT-base-patch16-224)
  • โ€”Layers 0-10: FROZEN (pretrained features)
  • โ€”Layer 11: UNFROZEN (learns generator-specific patterns)
  • โ€”Classifier: UNFROZEN (768 โ†’ 2)
  • โ€”Trainable Parameters: ~100,000
  • โ€”Training Data: 400 images (320 train / 40 val / 40 test)

Performance

MetricScore
Test Accuracy1.0000 (100.00%)
Precision1.0000
Recall1.0000
F1 Score1.0000

Features

โœ… Specialist detector (trained only on SeeDream) โœ… Unfrozen last attention layer for generator-specific features โœ… Test-Time Augmentation (TTA) enabled โœ… Heavy regularization (prevents overfitting on small dataset) โœ… H100-optimized training

Usage

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

model = ViTForImageClassification.from_pretrained("ash12321/seedream-4.5-specialist-h100")
processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")

image = Image.open("image.jpg")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    
if probs[0][1] > 0.5:
    print(f"AI-Generated (SeeDream): {probs[0][1]:.2%}")
else:
    print(f"Real: {probs[0][0]:.2%}")

Training Details

  • โ€”Training Time: 2.1 minutes
  • โ€”Best Epoch: 16
  • โ€”Device: H100 GPU
  • โ€”Unique data split (seed=456)

Limitations

โš ๏ธ This is a specialist detector trained ONLY for SeeDream.

Does NOT detect:

  • โ€”Other AI image generators
  • โ€”General synthetic images

For comprehensive AI detection, use as part of an ensemble with other specialist detectors.