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# microsoft-swinv2-small-patch4-window16-256-finetuned-xblockm

This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the [howdyaendra/xblock-social-screenshots](https://huggingface.co/datasets/howdyaendra/xblock-social-screenshots) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1252
- Roc Auc: 0.9535

## Model description

This model is trained on several thousand screenshots reported to the [XBlock 3rd-party Bluesky labeller service](https://bsky.app/profile/xblock.aendra.dev). It is 
intended to be used to label Bluesky posts that have screenshots from social media sites embedded in them. Please also see [aendra-rininsland/xblock](https://github.com/aendra-rininsland/xblock).

## Intended uses & limitations

Screenshot moderation

## Training and evaluation data

20% split of 1618 images

## Training procedure

See [notebook](https://github.com/aendra-rininsland/xblock-notebooks/blob/main/xblock-m.ipynb).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4357        | 0.9877 | 20   | 0.2544          | 0.7784  |
| 0.2027        | 1.9753 | 40   | 0.2016          | 0.8431  |
| 0.1743        | 2.9630 | 60   | 0.1701          | 0.8912  |
| 0.1625        | 4.0    | 81   | 0.1677          | 0.9083  |
| 0.1321        | 4.9877 | 101  | 0.1447          | 0.9246  |
| 0.1155        | 5.9753 | 121  | 0.1418          | 0.9311  |
| 0.0959        | 6.9630 | 141  | 0.1381          | 0.9460  |
| 0.0788        | 7.9012 | 160  | 0.1252          | 0.9535  |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.2.2
- Datasets 3.0.1
- Tokenizers 0.19.1
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