Downloads · 30 days
33
11% of all-time downloads
Melanoma-Cancer-Image-Classification
Hemg/Melanoma-Cancer-Image-Classification
Melanoma-Cancer-Image-Classification is a image classification model from Hemg. 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
290
Public
Parameters
85.8M
39.5 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
- Mar 12, 2024
- Updated
- Mar 18, 2024
- SHA
- d8915ab7
Base models
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. -->
Melanoma-Cancer-Image-Classification
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: 0.1954
- Accuracy: 0.9395
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: 3e-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: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5451 | 0.99 | 68 | 0.2960 | 0.8936 |
| 0.2488 | 1.99 | 137 | 0.2254 | 0.9105 |
| 0.1986 | 3.0 | 206 | 0.1913 | 0.9282 |
| 0.1714 | 4.0 | 275 | 0.1906 | 0.9264 |
| 0.1576 | 4.99 | 343 | 0.1825 | 0.9323 |
| 0.1359 | 5.99 | 412 | 0.1973 | 0.9318 |
| 0.1193 | 7.0 | 481 | 0.1756 | 0.9368 |
| 0.1062 | 8.0 | 550 | 0.1743 | 0.9382 |
| 0.0983 | 8.99 | 618 | 0.1885 | 0.9395 |
| 0.0797 | 9.99 | 687 | 0.1931 | 0.9309 |
| 0.0698 | 11.0 | 756 | 0.1895 | 0.9359 |
| 0.0657 | 12.0 | 825 | 0.1861 | 0.9368 |
| 0.0587 | 12.99 | 893 | 0.1837 | 0.9414 |
| 0.056 | 13.99 | 962 | 0.1936 | 0.9377 |
| 0.0592 | 15.0 | 1031 | 0.1958 | 0.935 |
| 0.0508 | 15.83 | 1088 | 0.1954 | 0.9395 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Mar 12, 2024
- Updated
- Mar 18, 2024