Update README.md

README.md

@@ -3,6 +3,7 @@ license: apache-2.0
 base_model: google/siglip-so400m-patch14-384
 tags:
 - generated_from_trainer
+- siglip
 metrics:
 - accuracy
 - f1
@@ -11,9 +12,6 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # siglip-tagger-test-3
 
 This model is a fine-tuned version of [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) on an unknown dataset.
@@ -24,15 +22,61 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is an experimental model that predicts danbooru tags of images.
+
+## Example
+
+```py
+from PIL import Image
+import torch
+
+from transformers import (
+    AutoModelForImageClassification,
+    AutoImageProcessor,
+)
+
+import numpy as np
+
+MODEL_NAME = "p1atdev/siglip-tagger-test-3"
+
+model = AutoModelForImageClassification.from_pretrained(
+    MODEL_NAME, torch_dtype=torch.bfloat16, trust_remote_code=True
+)
+model.eval()
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+
+image = Image.open("sample.jpg") # load your image
+
+inputs = processor(image, return_tensors="pt").to(model.device, model.dtype)
+
+logits = model(**inputs).logits.detach().cpu().float()[0]
+logits = np.clip(logits, 0.0, 1.0)
+
+results = {
+    model.config.id2label[i]: logit for i, logit in enumerate(logits) if logit > 0
+}
+results = sorted(results.items(), key=lambda x: x[1], reverse=True)
+
+for tag, score in results:
+    print(f"{tag}: {score*100:.2f}%")
+```
 
 ## Intended uses & limitations
 
-More information needed
+This model is for research use only and is not recommended for production. 
+
+Please use wd-v1-4-tagger series by SmilingWolf:
+
+- [SmilingWolf/wd-v1-4-moat-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)
+- [SmilingWolf/wd-v1-4-swinv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2)
+
+etc.
+
 
 ## Training and evaluation data
 
-More information needed
+High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500. (Same as p1atdev/siglip-tagger-test-2)
+
 
 ## Training procedure
 
@@ -109,4 +153,4 @@ The following hyperparameters were used during training:
 - Transformers 4.37.2
 - Pytorch 2.1.2+cu118
 - Datasets 2.16.1
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0