---
language: en
license: apache-2.0
tags:
- image-classification
- ai-detection
- pytorch
- resnet
- imager
datasets:
- Hemg/AI-Generated-vs-Real-Images-Datasets
- Rajarshi-Roy-research/Defactify_Image_Dataset
metrics:
- accuracy
- f1
- roc_auc
---

# Imager-1-Tiny

**Imager-1-Tiny** is a lightweight yet powerful image classifier designed to distinguish between real photographs and AI-generated images. It is part of the **IMAGER** family of open-source AI detection models. 

With only 25 million parameters, it offers an excellent trade-off between speed, memory consumption, and accuracy, making it ideal for resource-constrained environments.

## Model Details

- **Developed by:** Imagers
- **Model type:** ResNet-50 (Convolutional Neural Network)
- **Parameters:** ~25 Million
- **Input size:** 224 x 224 pixels
- **License:** Apache 2.0
- **Fine-tuned from:** ImageNet pre-trained weights
- **Optimal Threshold:** **0.385** (Crucial for correct inference)

## Intended Uses & Limitations

### Direct Use
This model is intended for classifying images as either **Real (0)** or **AI-Generated (1)**. It is optimized for speed and low memory consumption, making it ideal for edge devices, mobile applications, or backend servers with limited resources (like Railway's 512MB RAM free tier).

### Out-of-Scope Use
- **Medical or Legal Evidence:** Do not use this model as the sole evidence in legal or medical contexts. It is a statistical tool, not a forensic authority. Always combine it with human review.
- **Highly Compressed or Edited Images:** The model may struggle with images that have been heavily compressed (e.g., low-quality JPEGs), heavily filtered, or contain significant post-processing artifacts not seen during training.
- **Adversarial Attacks:** Like all current AI detectors, this model is vulnerable to adversarial noise specifically designed to fool it.

## Training Data

The model was trained on a combination of two large-scale datasets:
1. **Hemg/AI-Generated-vs-Real-Images-Datasets:** 152,710 images.
2. **Rajarshi-Roy-research/Defactify_Image_Dataset:** ~96,000 images (generated by DALL-E 3, Midjourney v6, SDXL, and SD3).

**Label Mapping:** 
- `0` = Real Image
- `1` = AI-Generated Image

### ⚖️ Legal & Dataset Licensing Note
While the model weights are released under the Apache 2.0 license, users must also comply with the original licenses of the training datasets. The **Defactify** dataset is licensed under MIT, while the **Hemg** dataset is intended for research purposes. Commercial use of this model should be reviewed against the original dataset terms to ensure full compliance.

## Training Procedure

- **Framework:** PyTorch, torchvision
- **Hardware:** Kaggle GPU environment (NVIDIA T4 or P100)
- **Epochs:** Early stopping triggered at Epoch 35 (no improvement for 8 consecutive epochs).
- **Total Training Time:** ~6.03 hours.
- **Optimization:** AdamW (Learning rate: `1e-4` or `2e-4` - *update if different*)
- **Loss Function:** BCEWithLogitsLoss
- **Batch Size:** 32 or 64 (*update if different*)
- **Precision:** FP32 (Full precision was used to ensure numerical stability).

## Evaluation Results

- **Validation Accuracy:** 97.03%
- **Macro F1-Score:** 0.9737
- **F1-Score (AI Class):** 0.9608
- **AUC-ROC:** 0.982

**Confusion Matrix (Validation Set):**
| | Predicted Real | Predicted AI |
|---|---|---|
| **Actual Real** | 11,937 | 239 |
| **Actual AI** | 442 | 10,288 |

*The model is slightly better at detecting Real images (98.0% recall) than AI images (95.9% recall).*

## How to Use

Because this is a custom PyTorch model, you need to load the architecture and weights manually. 

```python
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image

# 1. Define the architecture
def load_imager_tiny():
    model = models.resnet50(weights=None) # We load custom weights next
    # IMPORTANT: The final layer was replaced to output a single logit
    model.fc = nn.Linear(model.fc.in_features, 1)
    return model

# 2. Load weights
model = load_imager_tiny()
model.load_state_dict(torch.load("imager-1-tiny_best.pth", map_location="cpu"))
model.eval()

# 3. Preprocess image (Standard ImageNet normalization)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image = Image.open("your_image.jpg").convert("RGB")
input_tensor = preprocess(image).unsqueeze(0)

# 4. Inference
with torch.no_grad():
    logit = model(input_tensor)
    prob = torch.sigmoid(logit).item()

# 5. Apply the specific threshold (CRITICAL)
THRESHOLD = 0.385
prediction = "AI-Generated" if prob > THRESHOLD else "Real"
print(f"Probability of AI: {prob:.4f} -> Prediction: {prediction}")