---
library_name: onnx
pipeline_tag: image-classification
tags:
- onnx
- onnxruntime
- image-classification
- multi-label-classification
- efficientnet
- social-comparison
- on-device
- browser
- mobile
license: other
model-index:
- name: safesocial-trigger-classifier
  results:
  - task:
      type: image-classification
    dataset:
      name: primary Instagram-domain merged test (teacher-labeled)
      type: teacher-agreement
    metrics:
    - type: micro f1
      value: 0.6817
    - type: macro f1
      value: 0.5126
    - type: exact match
      value: 0.2952
    - type: label accuracy
      value: 0.8684
  - task:
      type: image-classification
    dataset:
      name: Karpathy COCO 500-image OOD holdout (teacher-labeled)
      type: teacher-agreement
    metrics:
    - type: micro f1
      value: 0.6526
    - type: macro f1
      value: 0.3466
    - type: exact match
      value: 0.5480
    - type: label accuracy
      value: 0.9176
---

# SafeSocial Trigger Classifier 

SafeSocial is integrated into [WebBrain](https://webbrain.one) as an on-device, privacy-preserving visual moderation and wellbeing layer that automatically detects and mitigates sensitive social-comparison triggers across user feeds before content is rendered.

![SafeSocial Demo](safesocial-unicef-readme.gif)

Compact on-device multi-label image classifier that detects Instagram-style
social-comparison triggers. Intended as a privacy-preserving, user-controlled
filtering aid — **not** a diagnostic tool, moderation authority, or measure of
human validity.

> All metrics below are **student-teacher agreement** against the Qwen teacher
> (`qwen3.6-35b-a3b`), not human-ground-truth accuracy. A human-verified
> evaluation set is still the main missing piece before making strong
> real-world claims.

## Files

- `model.onnx` — deployment model, opset 17, input `pixel_values` (NCHW float32), output `probabilities` (sigmoid multi-label)
- `safesocial-model.json` — machine-readable manifest: labels, per-label thresholds, preprocessing, checkpoint provenance, train/val metrics

SHA-256 of `model.onnx` at release: `fd37c1cd4aafa2bc318b7d29725fa6be3931d09cc1723bb30be3ee819ee22933`

## Labels (10, core taxonomy)

`romance_jealousy`, `social_fomo`, `luxury_status`, `travel_lifestyle`,
`body_beauty_comparison`, `achievement_status`, `social_proof_popularity`,
`exclusivity_access`, `none`, `uncertain`

Two labels from the earlier broad taxonomy were removed before this run and are
**not** predicted by this checkpoint: `envy` (too subjective / visually
underdetermined) and `financial_fomo` (too rare in the image sample).

## Performance (tuned per-label thresholds)

Primary merged test (core + Kaggle partial, unchanged benchmark split):

| metric | value |
| --- | ---: |
| micro F1 | 0.6817 |
| macro F1 | 0.5126 |
| exact match | 0.2952 |
| label accuracy | 0.8684 |

Separate Karpathy COCO 500-image OOD holdout:

| metric | value |
| --- | ---: |
| micro F1 | 0.6526 |
| macro F1 | 0.3466 |
| exact match | 0.5480 |
| label accuracy | 0.9176 |

Report the two domains separately. Do not mix COCO numbers into the primary
Instagram-domain benchmark.

## Thresholds (tuned on validation split, shipped in `safesocial-model.json`)

| label | threshold |
| --- | ---: |
| romance_jealousy | 0.75 |
| social_fomo | 0.80 |
| luxury_status | 0.45 |
| travel_lifestyle | 0.70 |
| body_beauty_comparison | 0.35 |
| achievement_status | 0.90 |
| social_proof_popularity | 0.60 |
| exclusivity_access | 0.60 |
| none | 0.75 |
| uncertain | 1.00 |

These thresholds need human review before product claims, especially for rare
labels.

## Preprocessing

- `image_size`: 224, `resize`: 256, center crop
- color: RGB, layout NCHW
- normalize with ImageNet mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]`
- input name: `pixel_values`, output name: `probabilities`

## Training

- backbone: `timm/tf_efficientnet_lite0` (feature dim 1280, trained end to end)
- teacher: `qwen3.6-35b-a3b` via local vLLM, core reduced taxonomy
- data: core `kkcosmos/instagram-images-with-captions` (19,991 valid rows) + partial `prithvijaunjale/instagram-images-with-captions` (2,641 rows) = 22,632 merged rows, plus capped ~20% Karpathy COCO auxiliary (4,500 train rows), 20 epochs
- this checkpoint is the aux20 20-epoch EfficientNet-Lite0 run from `paper/core_20k_findings.md`

## Quick use (Python + onnxruntime)

```python
import json
import numpy as np
import onnxruntime as ort
from PIL import Image

manifest = json.load(open("safesocial-model.json"))
labels = manifest["labels"]
thresholds = manifest["label_thresholds"]

img = Image.open("photo.jpg").convert("RGB")
img = img.resize((256, 256))
w, h = img.size
img = img.crop(((w-224)//2, (h-224)//2, (w+224)//2, (h+224)//2))
x = np.asarray(img, dtype=np.float32) / 255.0
mean = np.array([0.485, 0.456, 0.406], np.float32)
std = np.array([0.229, 0.224, 0.225], np.float32)
x = (x - mean) / std
x = np.transpose(x, (2, 0, 1))[None]

sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
probs = sess.run(None, {"pixel_values": x})[0][0]
print({l: float(p) for l, p in zip(labels, probs)})
print([l for l, p in zip(labels, probs) if p >= thresholds[l]])
```

Browser: ONNX Runtime Web (WebGPU/WASM). Mobile: ONNX Runtime React Native via
an Expo development build (Expo Go cannot run the native path); mock scores are
only a fallback. See the source repo `browser_extension/` and `mobile_app/`
prototypes.

## Limitations / data caveat

- Teacher-labeled test data measures agreement with the teacher, not human validity.
- `uncertain` had ~zero support in test splits.
- Public Instagram-style datasets often have unclear scraping/licensing histories: fine for exploratory research, but product training should prefer opt-in, licensed, or synthetic imagery.
- COCO/Flickr data is auxiliary out-of-domain robustness data, not primary benchmark evidence.
- Do not redistribute source Instagram/Kaggle/HF image files unless the source license explicitly allows it.
- No LICENSE file ships in the source repo at release time; confirm rights before commercial use.

## Source

Experiment details: `paper/core_20k_findings.md` in the SafeSocial repo. Model
export: `image_classifier/export_deployment_bundle.py`.


## License

This project is released under a **Private** license. For commercial licensing, integration inquiries, or permissions, please contact **WebBrain**.