p1atdev
/

wd-swinv2-tagger-v3-hf

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 ---
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 ---
 library_name: transformers
+license: apache-2.0
+base_model: SmilingWolf/wd-swinv2-tagger-v3
+inference: false
 ---
+# WD SwinV2 Tagger v3 with 🤗 transformers
+Converted from [SmilingWolf/wd-swinv2-tagger-v3](https://huggingface.co/SmilingWolf/wd-swinv2-tagger-v3) to transformers library format.
+## Example
+```py
+from PIL import Image
+import numpy as np
+import torch
+from transformers import (
+  AutoImageProcessor,
+  AutoModelForImageClassification,
+)
+MODEL_NAME = "p1atdev/wd-swinv2-tagger-v3-hf"
+model = AutoModelForImageClassification.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.bfloat16,
+)
+model.eval()
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
+image = Image.open("sample.webp")
+inputs = processor.preprocess(image, return_tensors="pt")
+outputs = model(**inputs.to(model.device, model.dtype))
+logits = torch.sigmoid(outputs.logits[0])
+# get probabilities
+results = {model.config.id2label[i]: logit.float() for i, logit in enumerate(logits)}
+results = {
+    k: v for k, v in sorted(results.items(), key=lambda item: item[1], reverse=True)
+}
+print(results)  # rating tags and character tags are also included
+#{'1girl': tensor(0.9968),
+# 'solo': tensor(0.9584),
+# 'dress': tensor(0.9418),
+# 'hat': tensor(0.9264),
+# 'sitting': tensor(0.9178),
+# 'looking_up': tensor(0.8978),
+# 'short_hair': tensor(0.8243),
+# 'sky': tensor(0.7846),
+# 'outdoors': tensor(0.7676),
+# 'rating:general': tensor(0.7562),
+# ...
+```