| --- |
| license: cc-by-nc-4.0 |
| library_name: timm |
| pipeline_tag: image-classification |
| base_model: timm/convnextv2_base.fcmae_ft_in22k_in1k_384 |
| tags: |
| - image-classification |
| - anime |
| - ai-generated-image-detection |
| - convnextv2 |
| --- |
| |
| <div align="center"> |
| <h1>Slopscan-Anime</h1> |
| <p><b>Detects if an anime image was generated by AI or drawn by a human.</b></p> |
|
|
| <p> |
| <a href="https://creativecommons.org/licenses/by-nc/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg" alt="License: CC BY-NC 4.0"></a> |
| <a href="https://github.com/Dominik7272/Slopscan"><img src="https://img.shields.io/badge/GitHub-Slopscan-181717?logo=github&logoColor=white" alt="GitHub"></a> |
| <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.11-blue?logo=python&logoColor=white" alt="Python 3.11"></a> |
| <a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-2.11-ee4c2c?logo=pytorch&logoColor=white" alt="PyTorch 2.11"></a> |
| </p> |
| </div> |
|
|
| --- |
|
|
| ## Highlights |
|
|
| **Slopscan-Anime** is a binary image classifier that separates AI-generated anime |
| art (Stable Diffusion / NovelAI / Midjourney / Flux / ...) from human-drawn art. |
| It is a fine-tune of ConvNeXt V2 Base at 1024×1024, tuned for high precision on |
| in-the-wild booru content. |
|
|
| - **0.9961 accuracy / 0.9998 AUROC** on a 29,390-image held-out test set |
| - Trained on 235,130 images with automatic label cleaning |
| - Runs on a consumer GPU (~1.2 GB VRAM, ~70 ms/image on an RTX 4070 Super) or CPU |
| - Single-file inference code, no framework dependencies beyond timm + torchvision |
|
|
| > **Note on scores:** training used label smoothing and mixup, so probabilities are |
| > compressed. AI images rarely score above ~0.92. Rank ordering is what matters. |
| > The recommended cutoff is **0.42**, lower it for more sensitivity, raise it for |
| > less false positives. |
|
|
| ## Model Overview |
|
|
| | | | |
| |---|---| |
| | Architecture | ConvNeXt V2 Base (`convnextv2_base.fcmae_ft_in22k_in1k_384`, 88.7M params) | |
| | Head | Linear, 2 logits | |
| | Input | RGB, resize to 1056px (shorter side), center crop 1024×1024 | |
| | Normalization | ImageNet mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]` | |
| | Output | P(AI generated), 0..1 | |
| | Weights in `best.pt` | EMA of the final fine-tune (1024px stage) | |
| | Recommended threshold | **0.42** | |
|
|
| ## Evaluation Results |
|
|
| Held-out test set, n = 29,390 (~50/50 class balance): |
|
|
| | Accuracy | Precision | Recall | F1 | AUROC | ECE | |
| |---|---|---|---|---|---| |
| | **0.9961** | **0.9951** | **0.9970** | **0.9961** | **0.9998** | 0.064 | |
|
|
| The threshold is set for F1 on validation; precision is slightly below recall by |
| design, so the tool errs toward catching AI images at the cost of a few |
| false positives. |
|
|
| ## Training Recipe |
|
|
| Data scraped from rule34.xxx via its public API: |
|
|
| | Split | Images | Source | |
| |---|---|---| |
| | Train | 235,130 | `ai_generated` tag (AI) vs. `-ai_generated -stable_diffusion -novelai -midjourney -flux -sdxl -dall-e -pixai -yodayo -comfyui sort:random` (human) | |
| | Val | 29,390 | same pipeline, stratified | |
| | Test | 29,390 | same pipeline, stratified | |
|
|
| All images were validated with Pillow and deduplicated by MD5 before splitting. |
|
|
| Fine-tuning warm-started from an earlier 512px checkpoint, then three progressive stages: |
|
|
| | Stage | Resolution | Batch size | LR | Epochs | Best val AUROC | |
| |---|---|---|---|---|---| |
| | 1 | 384 | 32 | 1.5e-5 | 20 | 0.9997 | |
| | 2 | 512 | 16 | 1e-5 | 5 | 0.9997 | |
| | 3 | 1024 | 4 | 1e-5 | 4 | 0.9999 | |
|
|
| Recipe: RandAugment, mixup 0.2, label smoothing 0.1, soft-target loss, EMA 0.999, |
| cosine LR with 5% warmup, bf16 autocast. A 384/512/1024 ensemble was evaluated and |
| did not beat the single 1024px model. |
|
|
| ## Quickstart |
|
|
| ```bash |
| git clone https://github.com/Dominik7272/Slopscan |
| cd Slopscan |
| pip install -r requirements.txt |
| pip install huggingface_hub |
| hf download Dominik72/Slopscan-Anime best.pt --local-dir . |
| ``` |
|
|
| ```python |
| from slopscan import Slopscan |
| |
| model = Slopscan("best.pt") # loads the model once, GPU if available |
| prob = model.classify("image.png") # P(AI generated), 0..1 |
| ``` |
|
|
| `classify` accepts a path or an already-open `PIL.Image`. CUDA is used when |
| available; expect ~70 ms/image on an RTX 4070 Super and ~1.2 GB VRAM. |
|
|
| ### ONNX |
|
|
| `best.onnx` from the same repo has the same weights and output (1024×1024 input, |
| raw logits) and runs without PyTorch, on onnxruntime alone: |
|
|
| ```python |
| import numpy as np |
| import onnxruntime as ort |
| from PIL import Image |
| |
| MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) |
| STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) |
| |
| image = Image.open("image.png").convert("RGB") |
| w, h = image.size |
| scale = 1056 / min(w, h) |
| image = image.resize((round(w * scale), round(h * scale)), Image.BILINEAR) |
| left, top = (image.width - 1024) // 2, (image.height - 1024) // 2 |
| image = image.crop((left, top, left + 1024, top + 1024)) |
| |
| x = np.asarray(image, dtype=np.float32) / 255.0 |
| x = (x - MEAN) / STD |
| x = x.transpose(2, 0, 1)[None] # NCHW |
| |
| session = ort.InferenceSession("best.onnx", providers=["CPUExecutionProvider"]) |
| logits = session.run(None, {"image": x})[0][0] |
| e = np.exp(logits - logits.max()) |
| prob = float(e[1] / e.sum()) # P(AI generated), 0..1 |
| ``` |
|
|
| ## Limitations and Disclaimer |
|
|
| - **Domain**: trained on anime illustration art. It will not generalize to photos, |
| photorealistic renderings, or non-anime styles. |
| - **3D renders**: human-made 3D work (Blender / MMD / SFM / Daz) is sometimes |
| flagged as AI. This is a known weakness and the target of the next model version. |
| - **Label noise**: the `ai_generated` tag is applied by uploaders; untagged AI |
| uploads land in the human set. The ~0.4% test error roughly bounds this noise. |
| - **Distribution shift**: new generators and artist mimicry can be misclassified. |
| - A prediction is a hint, not proof about a creator's process. Do not use this |
| model to harass artists or to make definitive accusations. |
|
|
| ## License |
|
|
| **CC BY-NC 4.0** non-commercial use only. This model is a fine-tune of FAIR's |
| ConvNeXt V2 weights, which are released under CC BY-NC 4.0, so the derivative |
| inherits the same restriction. Training images were scraped from publicly viewable |
| rule34.xxx pages and are not redistributed here. |
|
|
| ## Citation |
|
|
| If you use this model, please cite the backbone it is built on: |
|
|
| ```bibtex |
| @article{Woo2023ConvNeXtV2, |
| title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, |
| author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie}, |
| year={2023}, |
| journal={arXiv preprint arXiv:2301.00808}, |
| } |
| |
| @misc{rw2019timm, |
| author = {Ross Wightman}, |
| title = {PyTorch Image Models}, |
| year = {2019}, |
| publisher = {GitHub}, |
| journal = {GitHub repository}, |
| doi = {10.5281/zenodo.4414861}, |
| howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} |
| } |
| ``` |
|
|
