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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
base_model: google/vit-large-patch32-384
model-index:
- name: vit-large-patch32-384-finetuned-melanoma
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.8272727272727273
name: Accuracy
---
<!-- 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. -->
# vit-large-patch32-384-finetuned-melanoma
This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0767
- Accuracy: 0.8273
## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0081 | 1.0 | 550 | 0.7650 | 0.68 |
| 0.7527 | 2.0 | 1100 | 0.6693 | 0.7364 |
| 0.6234 | 3.0 | 1650 | 0.6127 | 0.7709 |
| 2.6284 | 4.0 | 2200 | 0.6788 | 0.7655 |
| 0.1406 | 5.0 | 2750 | 0.6657 | 0.7836 |
| 0.317 | 6.0 | 3300 | 0.6936 | 0.78 |
| 2.5358 | 7.0 | 3850 | 0.7104 | 0.7909 |
| 1.5802 | 8.0 | 4400 | 0.6928 | 0.8 |
| 0.088 | 9.0 | 4950 | 0.8060 | 0.7982 |
| 0.0183 | 10.0 | 5500 | 0.7811 | 0.8091 |
| 0.0074 | 11.0 | 6050 | 0.7185 | 0.7945 |
| 0.0448 | 12.0 | 6600 | 0.8780 | 0.7909 |
| 0.4288 | 13.0 | 7150 | 0.8229 | 0.82 |
| 0.017 | 14.0 | 7700 | 0.7516 | 0.8182 |
| 0.0057 | 15.0 | 8250 | 0.7974 | 0.7964 |
| 1.7571 | 16.0 | 8800 | 0.7866 | 0.8218 |
| 1.3159 | 17.0 | 9350 | 0.8491 | 0.8073 |
| 1.649 | 18.0 | 9900 | 0.8432 | 0.7891 |
| 0.0014 | 19.0 | 10450 | 0.8870 | 0.82 |
| 0.002 | 20.0 | 11000 | 0.9460 | 0.8236 |
| 0.3717 | 21.0 | 11550 | 0.8866 | 0.8327 |
| 0.0025 | 22.0 | 12100 | 1.0287 | 0.8073 |
| 0.0094 | 23.0 | 12650 | 0.9696 | 0.8091 |
| 0.002 | 24.0 | 13200 | 0.9659 | 0.8018 |
| 0.1001 | 25.0 | 13750 | 0.9712 | 0.8327 |
| 0.2953 | 26.0 | 14300 | 1.0512 | 0.8236 |
| 0.0141 | 27.0 | 14850 | 1.0503 | 0.82 |
| 0.612 | 28.0 | 15400 | 1.2020 | 0.8109 |
| 0.0792 | 29.0 | 15950 | 1.0498 | 0.8364 |
| 0.0117 | 30.0 | 16500 | 1.0079 | 0.8327 |
| 0.0568 | 31.0 | 17050 | 1.0199 | 0.8255 |
| 0.0001 | 32.0 | 17600 | 1.0319 | 0.8291 |
| 0.075 | 33.0 | 18150 | 1.0427 | 0.8382 |
| 0.001 | 34.0 | 18700 | 1.1289 | 0.8382 |
| 0.0001 | 35.0 | 19250 | 1.0589 | 0.8364 |
| 0.0006 | 36.0 | 19800 | 1.0349 | 0.8236 |
| 0.0023 | 37.0 | 20350 | 1.1192 | 0.8273 |
| 0.0002 | 38.0 | 20900 | 1.0863 | 0.8273 |
| 0.2031 | 39.0 | 21450 | 1.0604 | 0.8255 |
| 0.0006 | 40.0 | 22000 | 1.0767 | 0.8273 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
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