Cortexa Marketing Scorer

Pipeline

ad image ─→ CLIP ViT-B/32 vision encoder (fp16 ONNX) ─→ (1, 512) stim_emb
demographic ─→ CLIP text encoder       (fp16 ONNX) ─→ (1, 512) demo_emb
                                  ↓ concat
                              cortexa_v10 head (ONNX)
                                  ↓
                         4 metric heads → z-scores

Demographic amplification (×12 over the "general audience" baseline) is documented inline in the serving Space's inference.py.

Files

Reference inference

See M725/cortexa-ad-api's app/inference.py::CortexaPipeline for the canonical CPU inference path.

License

Pleius internal — see https://pleius.com. Not for redistribution.

cortexa_v10.onnx

onnx · 3.56 MB · SHA-256 add9910a147d…63f6 · Hugging Face

--- language: - en license: other license_name: pleius-internal tags: - onnx - image-classification - ad-scoring - creator-tools --- # cortexa-marketing-scorer (v10) Production scorer for Pleius Marketing. Given an ad image and a target demographic, returns 4 z-scored pillars: - `universal_appeal` — does it work for everyone - `demographic_appeal` — does it work for the *target* slice (amplified against a "general audience" baseline; see `inference.py` in the serving Space `M725/cortexa-ad-api`) - `audience_drive` — likelihood that the target acts on the ad - `engagement` — saveable / shareable / rewatchable ## Pipeline ``` ad image ─→ CLIP ViT-B/32 vision encoder (fp16 ONNX) ─→ (1, 512) stim_emb demographic ─→ CLIP text encoder (fp16 ONNX) ─→ (1, 512) demo_emb ↓ concat cortexa_v10 head (ONNX) ↓ 4 metric heads → z-scores ``` Demographic amplification (×12 over the "general audience" baseline) is documented inline in the serving Space's `inference.py`. ## Files | file | purpose | |---|---| | `clip_vision_fp16.onnx` | CLIP ViT-B/32 vision tower, fp16. Disable graph opts (SimplifiedLayerNormFusion bug with fp16 Cast graphs). | | `clip_text_fp16.onnx` | CLIP text tower, fp16. Same caveat. | | `cortexa_v10.onnx` | Pleius-trained metric head. Takes (demo_emb, stim_emb) → 4 metric tensors in METRICS order. | | `clip_tokenizer.json` | HuggingFace `tokenizers` JSON. Pad to 77, truncate at 77. | ## Reference inference See `M725/cortexa-ad-api`'s `app/inference.py::CortexaPipeline` for the canonical CPU inference path. ## License Pleius internal — see https://pleius.com. Not for redistribution.