Downloads · 30 days
29
4% of all-time downloads
swin-tiny-patch4-window7-224-vit-finetuned-melanoma
ThankGod/swin-tiny-patch4-window7-224-vit-finetuned-melanoma
swin-tiny-patch4-window7-224-vit-finetuned-melanoma is a image classification model from ThankGod. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
672
Public
Repo size
5.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin110 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- swin
- Access
- Public
- Created
- Aug 19, 2023
- Updated
- Aug 21, 2023
- SHA
- 1f0182c5
Model card
From the Hugging Face model README
<!-- 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. -->
swin-tiny-patch4-window7-224-vit-finetuned-melanoma
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2687
- Accuracy: 0.9017
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5392 | 1.0 | 112 | 0.5796 | 0.7884 |
| 0.4716 | 2.0 | 224 | 0.4380 | 0.8304 |
| 0.3932 | 3.0 | 336 | 0.3749 | 0.8534 |
| 0.3446 | 4.0 | 448 | 0.3851 | 0.8423 |
| 0.3401 | 5.0 | 560 | 0.3141 | 0.8708 |
| 0.2842 | 6.0 | 672 | 0.3309 | 0.8685 |
| 0.3126 | 7.0 | 784 | 0.3493 | 0.8629 |
| 0.2748 | 8.0 | 896 | 0.3391 | 0.8772 |
| 0.2455 | 9.0 | 1008 | 0.3053 | 0.8843 |
| 0.2361 | 10.0 | 1120 | 0.2749 | 0.8954 |
| 0.2218 | 11.0 | 1232 | 0.2842 | 0.8994 |
| 0.2071 | 12.0 | 1344 | 0.2507 | 0.9089 |
| 0.2228 | 13.0 | 1456 | 0.2614 | 0.8994 |
| 0.2018 | 14.0 | 1568 | 0.2664 | 0.9105 |
| 0.1774 | 15.0 | 1680 | 0.2687 | 0.9017 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- swin
- License
- apache-2.0
- Created
- Aug 19, 2023
- Updated
- Aug 21, 2023