Update: NonCommercial license emphasis + v9.4 fair eval metrics

README.md

@@ -14,6 +14,11 @@ pipeline_tag: audio-classification
 
 ArtifactNet detects AI-generated music by extracting forensic residual artifacts via a task-specific UNet, rather than learning generator-specific patterns. This approach generalizes across 22 AI music generators with only 4.2M parameters.
 
+> ⚠️ **License: CC BY-NC 4.0 — Non-Commercial Only**
+> This model and its weights may not be used for any commercial product, service, API, or
+> revenue-generating activity. Research, academic, and personal evaluation use are welcome.
+> For commercial licensing, contact: **unohee.official@gmail.com**
+
 ## Model Description
 
 - **Architecture**: ArtifactUNet (3.6M) + 7ch HPSS Forensic CNN (424K) = 4.2M total
@@ -21,17 +26,19 @@ ArtifactNet detects AI-generated music by extracting forensic residual artifacts
 - **Output**: P(AI) ∈ [0, 1] per segment, song-level median verdict
 - **Format**: Single ONNX file (entire pipeline: STFT → UNet → HPSS → 7ch → CNN → sigmoid)
 
-## Performance (ArtifactBench v1, fair eval)
+## Performance — ArtifactBench v0.9 (test-only fair eval, all models unseen)
 
 | Metric | ArtifactNet (4.2M) | CLAM (194M) | SpecTTTra (19M) |
 |---|---|---|---|
-| **F1** | **0.983** | 0.824 | 0.766 |
-| **Precision** | 0.991 | 0.758 | 0.885 |
-| **Recall (TPR)** | 0.976 | 0.904 | 0.675 |
-| **FPR** | 0.015 | 0.705 | 0.214 |
+| **F1** | **0.9829** | 0.7576 | 0.7713 |
+| **Precision** | 0.9905 | 0.6674 | 0.8519 |
+| **Recall (TPR)** | 0.9755 | 0.8761 | 0.7046 |
+| **FPR** | 0.0149 | 0.6926 | 0.1943 |
+| **AUC** | **0.9974** | 0.7031 | 0.8460 |
 | @FPR≤5% TPR | **99.1%** | - | - |
 
-Evaluated on 8,766 tracks across 22 AI generators and 6 real music sources.
+Evaluated on 2,263 tracks (`bench_origin=test`, unseen by all three models),
+threshold τ=0.5, identical preprocessing.
 
 ## Usage
 
@@ -58,7 +65,7 @@ For song-level verdict, compute median over multiple chunks.
 
 ## Benchmark
 
-Evaluate with [ArtifactBench v1](https://huggingface.co/datasets/intrect/artifactbench-v1).
+Evaluate with [ArtifactBench v1](https://huggingface.co/datasets/intrect/artifactbench).
 
 ## Citation
 
@@ -72,4 +79,12 @@ Evaluate with [ArtifactBench v1](https://huggingface.co/datasets/intrect/artifac
 
 ## License
 
-CC BY-NC 4.0
+**CC BY-NC 4.0** — Free for academic, research, and personal use. **Commercial use is
+prohibited** without prior written permission. This includes (but is not limited to):
+
+- Selling access to the model or its outputs
+- Integrating into commercial products, SaaS, or APIs
+- Using the model to generate revenue, directly or indirectly
+- Training derivative commercial models on these weights
+
+For commercial licensing inquiries: **unohee.official@gmail.com**