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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- recall
model-index:
- name: vca
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Recall
type: recall
value: 0.6533333333333333
---
<!-- 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. -->
# vca
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: 0.2071
- Recall: 0.6533
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 9 | 0.6362 | 0.3333 |
| No log | 2.0 | 18 | 0.4641 | 0.0 |
| No log | 3.0 | 27 | 0.3251 | 0.0 |
| No log | 4.0 | 36 | 0.2605 | 0.0 |
| No log | 5.0 | 45 | 0.2100 | 0.0 |
| No log | 6.0 | 54 | 0.1943 | 0.08 |
| No log | 7.0 | 63 | 0.1986 | 0.64 |
| No log | 8.0 | 72 | 0.1856 | 0.6933 |
| No log | 9.0 | 81 | 0.1654 | 0.6933 |
| No log | 10.0 | 90 | 0.1593 | 0.72 |
| No log | 11.0 | 99 | 0.1638 | 0.68 |
| No log | 12.0 | 108 | 0.1732 | 0.6933 |
| No log | 13.0 | 117 | 0.1748 | 0.56 |
| No log | 14.0 | 126 | 0.1792 | 0.6533 |
| No log | 15.0 | 135 | 0.1743 | 0.84 |
| No log | 16.0 | 144 | 0.1760 | 0.5733 |
| No log | 17.0 | 153 | 0.1641 | 0.6 |
| No log | 18.0 | 162 | 0.1558 | 0.76 |
| No log | 19.0 | 171 | 0.2121 | 0.7867 |
| No log | 20.0 | 180 | 0.1765 | 0.56 |
| No log | 21.0 | 189 | 0.1802 | 0.7733 |
| No log | 22.0 | 198 | 0.1729 | 0.7467 |
| No log | 23.0 | 207 | 0.2004 | 0.48 |
| No log | 24.0 | 216 | 0.1794 | 0.72 |
| No log | 25.0 | 225 | 0.2185 | 0.7867 |
| No log | 26.0 | 234 | 0.2115 | 0.8533 |
| No log | 27.0 | 243 | 0.1999 | 0.7067 |
| No log | 28.0 | 252 | 0.1900 | 0.5467 |
| No log | 29.0 | 261 | 0.2158 | 0.72 |
| No log | 30.0 | 270 | 0.2515 | 0.8533 |
| No log | 31.0 | 279 | 0.2322 | 0.7733 |
| No log | 32.0 | 288 | 0.2024 | 0.8 |
| No log | 33.0 | 297 | 0.2342 | 0.76 |
| No log | 34.0 | 306 | 0.2205 | 0.7467 |
| No log | 35.0 | 315 | 0.1820 | 0.7067 |
| No log | 36.0 | 324 | 0.2169 | 0.68 |
| No log | 37.0 | 333 | 0.2170 | 0.6133 |
| No log | 38.0 | 342 | 0.1767 | 0.68 |
| No log | 39.0 | 351 | 0.2326 | 0.8133 |
| No log | 40.0 | 360 | 0.2386 | 0.76 |
| No log | 41.0 | 369 | 0.2431 | 0.68 |
| No log | 42.0 | 378 | 0.2160 | 0.6933 |
| No log | 43.0 | 387 | 0.2234 | 0.76 |
| No log | 44.0 | 396 | 0.2491 | 0.7467 |
| No log | 45.0 | 405 | 0.2342 | 0.6933 |
| No log | 46.0 | 414 | 0.2124 | 0.7333 |
| No log | 47.0 | 423 | 0.2602 | 0.6533 |
| No log | 48.0 | 432 | 0.2702 | 0.6133 |
| No log | 49.0 | 441 | 0.2258 | 0.6533 |
| No log | 50.0 | 450 | 0.2158 | 0.64 |
| No log | 51.0 | 459 | 0.2071 | 0.6533 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|