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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: urinary_carcinoma_classifier_g001
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train[:33]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8571428571428571
---
<!-- 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. -->
# urinary_carcinoma_classifier_g001
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3828
- Accuracy: 0.8571
## 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: 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.6854 | 0.7143 |
| No log | 2.0 | 2 | 0.6759 | 0.5714 |
| No log | 3.0 | 3 | 0.6738 | 0.5714 |
| No log | 4.0 | 4 | 0.6571 | 0.5714 |
| No log | 5.0 | 5 | 0.6342 | 0.5714 |
| No log | 6.0 | 6 | 0.6339 | 0.5714 |
| No log | 7.0 | 7 | 0.5402 | 0.5714 |
| No log | 8.0 | 8 | 0.5827 | 0.5714 |
| No log | 9.0 | 9 | 0.5439 | 0.7143 |
| 0.2718 | 10.0 | 10 | 0.5553 | 0.7143 |
| 0.2718 | 11.0 | 11 | 0.4241 | 1.0 |
| 0.2718 | 12.0 | 12 | 0.5177 | 0.8571 |
| 0.2718 | 13.0 | 13 | 0.4088 | 0.8571 |
| 0.2718 | 14.0 | 14 | 0.4763 | 0.7143 |
| 0.2718 | 15.0 | 15 | 0.3164 | 1.0 |
| 0.2718 | 16.0 | 16 | 0.3087 | 1.0 |
| 0.2718 | 17.0 | 17 | 0.3457 | 0.8571 |
| 0.2718 | 18.0 | 18 | 0.2585 | 1.0 |
| 0.2718 | 19.0 | 19 | 0.3642 | 0.8571 |
| 0.1299 | 20.0 | 20 | 0.4421 | 0.7143 |
| 0.1299 | 21.0 | 21 | 0.3558 | 0.8571 |
| 0.1299 | 22.0 | 22 | 0.3611 | 0.8571 |
| 0.1299 | 23.0 | 23 | 0.5796 | 0.7143 |
| 0.1299 | 24.0 | 24 | 0.4137 | 0.8571 |
| 0.1299 | 25.0 | 25 | 0.4281 | 0.8571 |
| 0.1299 | 26.0 | 26 | 0.2066 | 1.0 |
| 0.1299 | 27.0 | 27 | 0.2251 | 1.0 |
| 0.1299 | 28.0 | 28 | 0.2459 | 1.0 |
| 0.1299 | 29.0 | 29 | 0.4450 | 0.8571 |
| 0.0743 | 30.0 | 30 | 0.3828 | 0.8571 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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