license: apache-2.0
base_model: xinyu1205/recognize-anything-plus-model
tags:
- onnx
- image-classification
- tagging
library_name: onnx
pipeline_tag: image-classification
RAM++ (Recognize Anything Plus) — ONNX export
ONNX export of xinyu1205/recognize-anything-plus-model
(ram_plus_swin_large_14m.pth, revision 84d4aee3a0265c4e0df1f714f0572011d1bf2ec3), for CPU
inference in Mendako's tagging sidecar.
All credit for the model belongs to its authors. This repository adds no training and no weights of its own — it is the same network, exported.
Files
| File | Description |
|---|---|
model.onnx |
fp32 graph. Input image [1, 3, 384, 384] float32 → output logits [1, 4585] float32 |
tags.txt |
4585 tag names, one per line, in output order |
thresholds.txt |
RAM++'s own per-tag decision threshold, one float per line, same order |
Usage
Preprocess exactly as upstream's get_transform does — resize to 384×384 squashed (no
aspect-ratio padding, bilinear), then normalize with the ImageNet statistics:
import numpy as np, onnxruntime as ort
from PIL import Image
MEAN = (0.485, 0.456, 0.406)
STD = (0.229, 0.224, 0.225)
image = Image.open("photo.jpg").convert("RGB").resize((384, 384), Image.BILINEAR)
x = np.asarray(image, dtype=np.float32) / 255.0
x = (x - np.asarray(MEAN, dtype=np.float32)) / np.asarray(STD, dtype=np.float32)
x = np.ascontiguousarray(x.transpose(2, 0, 1)[np.newaxis, ...])
session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
logits = np.asarray(session.run(None, {"image": x})[0]).reshape(-1)
tags = [line.strip() for line in open("tags.txt", encoding="utf-8")]
thresholds = [float(line) for line in open("thresholds.txt", encoding="utf-8")]
scores = 1.0 / (1.0 + np.exp(-np.clip(logits.astype(np.float64), -30.0, 30.0)))
fired = [tag for tag, score, t in zip(tags, scores, thresholds) if score > t]
Each tag is scored independently — this is multi-label classification, not a softmax over classes.
Compare with strict >: a few tags ship a threshold of 1.0, which is how RAM++ disables them,
and a saturated logit rounded in float32 would otherwise revive them.
Verification
The export script refuses to write anything unless the result reproduces upstream's own tagging
decision. On upstream's images/demo/demo1.jpg:
- max
|eager − onnx|logit drift: 1.5×10⁻⁵ - tags produced: identical set of 19, compared against
ram.inference_ram()
Reproducing
See ml/tools/export_ram_plus.py
in the Mendako repository.
License
Apache-2.0, inherited from the upstream model (© OPPO). See the Recognize Anything repository for the paper and original code.