Imager-1-Pro
Imager-1-Pro is the high-capacity model of the Imager family: an AI-generated image detector with 89M parameters, built on ConvNeXt-Base and trained on Defactify (DALL-E 3, Midjourney v6, SDXL, Stable Diffusion 3).
Try it
imager.cc.cd
Upload an image and get an instant prediction
with confidence scores for both AI-generated and Real classes.
Results
| Accuracy | F1-IA | AUC | TPR@1%FPR | Parameters |
|---|---|---|---|---|
| 99.33% | 99.60% | 99.78% | 99.00% | 89M |
Validation Accuracy: 99.33% — evaluated on a stratified group split (no data leakage between train/val/test).
F1-IA: 99.60% — F1 score for the AI-generated class, the most relevant metric for detection.
AUC: 99.78% — area under the ROC curve, showing strong separability between classes.
TPR@1%FPR: 99.00% — detects 99% of AI images while keeping false positives below 1%.
Parameters: 89M — optimized for high-accuracy deployment on servers with ONNX support.
Architecture
| Field | Value |
|---|---|
| Base | ConvNeXt-Base |
| Parameters | 89M |
| Pretrained weights | ImageNet-1K |
| Input size | 224 × 224 RGB |
| Framework | PyTorch |
| Output | 2 classes |
Training
| Field | Value |
|---|---|
| Dataset | Defactify (DALL-E 3, Midjourney v6, SDXL, SD3) |
| Optimizer | AdamW |
| Learning rate | 1e-4 (backbone) / 1e-3 (head) |
| Weight decay | 0.05 |
| Scheduler | Cosine with warmup + re-warmup on unfreeze |
| Loss | CrossEntropyLoss (label_smoothing=0.05) |
| Batch size | 32 (effective 64 with grad accumulation) |
| Precision | AMP FP16 |
| Gradient clipping | 1.0 |
| Augmentation | JPEG, down/up sampling, blur, crop, flip |
| EMA decay | 0.999 |
| Training time | 11.5 hours |
About this release
This repository contains the base model of Imager-1-Pro, released for developers and researchers who want direct access to the weights and training pipeline.
Imager always publishes a separately verified and production-ready version of each model, tested against real-world generators before release. This base model is provided so the community can build on top of it, fine-tune it, or integrate it into their own systems.
Access to the most powerful Imager models is always granted through the official channels.
Task
Binary image classification.
| Label | Meaning |
|---|---|
0 | Real |
1 | AI-generated |