p1atdev / siglip-tagger-test-2

siglip-tagger-test-2

This model is a fine-tuned version of google/siglip-base-patch16-512 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 364.7850
• Accuracy: 0.2539
• F1: 0.9967

Model description

This model is an experimental model that predicts danbooru tags of images.

Example

from PIL import Image

import torch
from transformers import (
    AutoModelForImageClassification,
    AutoImageProcessor,
)
import numpy as np

MODEL_NAME = "p1atdev/siglip-tagger-test-2"

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}%")
# 1girl: 100.00%
# outdoors: 100.00%
# sky: 100.00%
# solo: 100.00%
# school uniform: 96.88%
# skirt: 92.97%
# day: 89.06%
# ...

Intended uses & limitations

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
• SmilingWolf/wd-v1-4-swinv2-tagger-v2

etc.

Training and evaluation data

High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 20

Training results

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0