|
--- |
|
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 |
