---
license: apache-2.0
tags:
- generated_from_trainer
- siglip
metrics:
- accuracy
- f1
base_model: google/siglip-base-patch16-512
model-index:
- name: siglip-tagger-test-2
  results: []
pipeline_tag: image-classification
---

<!-- 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-2

This model is a fine-tuned version of [google/siglip-base-patch16-512](https://huggingface.co/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

```py
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](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

High quality 5000 images from danbooru. They were shulled 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

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1496.9876     | 1.0   | 141  | 691.3267        | 0.1242   | 0.9957 |
| 860.0218      | 2.0   | 282  | 433.5286        | 0.1626   | 0.9965 |
| 775.4277      | 3.0   | 423  | 409.0374        | 0.1827   | 0.9966 |
| 697.2465      | 4.0   | 564  | 396.5604        | 0.2025   | 0.9966 |
| 582.6023      | 5.0   | 705  | 388.3294        | 0.2065   | 0.9966 |
| 617.5087      | 6.0   | 846  | 382.2605        | 0.2213   | 0.9966 |
| 627.533       | 7.0   | 987  | 377.6726        | 0.2269   | 0.9967 |
| 595.4033      | 8.0   | 1128 | 374.3268        | 0.2327   | 0.9967 |
| 593.3854      | 9.0   | 1269 | 371.4181        | 0.2409   | 0.9967 |
| 537.9777      | 10.0  | 1410 | 369.5010        | 0.2421   | 0.9967 |
| 552.3083      | 11.0  | 1551 | 368.0743        | 0.2468   | 0.9967 |
| 570.5438      | 12.0  | 1692 | 366.8302        | 0.2498   | 0.9967 |
| 507.5343      | 13.0  | 1833 | 366.1787        | 0.2499   | 0.9967 |
| 515.5528      | 14.0  | 1974 | 365.5653        | 0.2525   | 0.9967 |
| 458.5096      | 15.0  | 2115 | 365.1838        | 0.2528   | 0.9967 |
| 515.6953      | 16.0  | 2256 | 364.9844        | 0.2535   | 0.9967 |
| 533.7929      | 17.0  | 2397 | 364.8577        | 0.2538   | 0.9967 |
| 520.3728      | 18.0  | 2538 | 364.8066        | 0.2537   | 0.9967 |
| 525.1097      | 19.0  | 2679 | 364.7850        | 0.2539   | 0.9967 |
| 482.0612      | 20.0  | 2820 | 364.7876        | 0.2539   | 0.9967 |


### Framework versions

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