howdyaendra/microsoft-swinv2-small-patch4-window16-256-finetuned-xblockm
By howdyaendra · ↓ 7 · ♥ 0 · Updated 2024-12-22
What's in this repo
This page lists the files in howdyaendra/microsoft-swinv2-small-patch4-window16-256-finetuned-xblockm, including model.onnx, model_quantized.onnx.
- 2 ONNX models (.onnx files)
- 12 files, including 6 .json, 2 .onnx, 1 .md, 1 .safetensors, 1 .bin
- Last updated 2024-12-22
ONNX files
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
microsoft-swinv2-small-patch4-window16-256-finetuned-xblockm
This model is a fine-tuned version of microsoft/swinv2-small-patch4-window16-256 on the 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. 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.
Intended uses & limitations
Screenshot moderation
Training and evaluation data
20% split of 1618 images
Training procedure
See notebook.
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
Model info
Created: 2024-10-09
Updated: 2024-12-22
View on Hugging Face