File size: 6,449 Bytes
d39182d 9302829 d39182d f0ee541 d39182d f0ee541 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | ---
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.
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**.
|