---
title: watermark_detector
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/watermark_detector"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/watermark_detector.md"
repository: AI-ModelScope/watermark_detector
last_updated: 2024-07-26
license: apache-2.0
model_type:
  - vit
architectures:
  - ViTForImageClassification
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 2467
stars: 0
tags:
  - generated_from_trainer
---

# watermark_detector

> watermark_detector - AI-ModelScope 在 ModelScope 开源的模型。This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.6014 Accuracy: 0.6574

AI-ModelScope/watermark_detector 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: AI-ModelScope/watermark_detector
- **License**: apache-2.0
- **Tags**: generated_from_trainer
- **Downloads**: 2467
- **Stars**: 0
- **Last updated**: 2024-07-26

Source: https://www.modelscope.cn/models/AI-ModelScope/watermark_detector

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# watermark_detector

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6014
- Accuracy: 0.6574

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6492        | 1.0   | 1139 | 0.6375          | 0.6262   |
| 0.6172        | 2.0   | 2278 | 0.6253          | 0.6438   |
| 0.578         | 3.0   | 3417 | 0.6110          | 0.6508   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3