Downloads · 30 days
17
12% of all-time downloads
isic2018-melanoma-vit-baseline
oscar2525mv/isic2018-melanoma-vit-baseline
isic2018-melanoma-vit-baseline is a image classification model from oscar2525mv. 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
147
Public
Parameters
85.8M
1.7 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors343 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Jan 5, 2026
- Updated
- Jan 7, 2026
- SHA
- 2e375562
Base models
Spaces
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. -->
isic2018-melanoma-vit-baseline
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2496
- Auc: 0.9126
- F1: 0.5460
- Recall Melanoma: 0.4433
- Precision: 0.7107
- Tn: 1262
- Fp: 35
- Fn: 108
- Tp: 86
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Auc | F1 | Recall Melanoma | Precision | Tn | Fp | Fn | Tp |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2132 | 1.0 | 533 | 0.2888 | 0.8683 | 0.4786 | 0.4330 | 0.5350 | 1224 | 73 | 110 | 84 |
| 0.2498 | 2.0 | 1066 | 0.2524 | 0.9011 | 0.5180 | 0.4072 | 0.7117 | 1265 | 32 | 115 | 79 |
| 0.1937 | 3.0 | 1599 | 0.2497 | 0.9036 | 0.4659 | 0.3351 | 0.7647 | 1277 | 20 | 129 | 65 |
| 0.1668 | 4.0 | 2132 | 0.2432 | 0.9125 | 0.5590 | 0.4639 | 0.7031 | 1259 | 38 | 104 | 90 |
| 0.1612 | 5.0 | 2665 | 0.2496 | 0.9126 | 0.5460 | 0.4433 | 0.7107 | 1262 | 35 | 108 | 86 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Jan 5, 2026
- Updated
- Jan 7, 2026