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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-agrivision
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9202733485193622
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-agrivision
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3605
- Accuracy: 0.9203
## 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: 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5913 | 1.0 | 31 | 0.7046 | 0.7175 |
| 0.1409 | 2.0 | 62 | 0.8423 | 0.6788 |
| 0.0825 | 3.0 | 93 | 0.6224 | 0.7654 |
| 0.0509 | 4.0 | 124 | 0.4379 | 0.8360 |
| 0.0439 | 5.0 | 155 | 0.1706 | 0.9317 |
| 0.0107 | 6.0 | 186 | 0.1914 | 0.9362 |
| 0.0134 | 7.0 | 217 | 0.2491 | 0.9089 |
| 0.0338 | 8.0 | 248 | 0.2119 | 0.9362 |
| 0.0306 | 9.0 | 279 | 0.4502 | 0.8610 |
| 0.0054 | 10.0 | 310 | 0.4990 | 0.8747 |
| 0.0033 | 11.0 | 341 | 0.2746 | 0.9112 |
| 0.0021 | 12.0 | 372 | 0.2501 | 0.9317 |
| 0.0068 | 13.0 | 403 | 0.1883 | 0.9522 |
| 0.0038 | 14.0 | 434 | 0.3672 | 0.9134 |
| 0.0006 | 15.0 | 465 | 0.2275 | 0.9408 |
| 0.0011 | 16.0 | 496 | 0.3349 | 0.9134 |
| 0.0017 | 17.0 | 527 | 0.3329 | 0.9157 |
| 0.0007 | 18.0 | 558 | 0.2508 | 0.9317 |
| 0.0023 | 19.0 | 589 | 0.2338 | 0.9385 |
| 0.0003 | 20.0 | 620 | 0.3193 | 0.9226 |
| 0.002 | 21.0 | 651 | 0.4604 | 0.9043 |
| 0.0023 | 22.0 | 682 | 0.3338 | 0.9203 |
| 0.005 | 23.0 | 713 | 0.2925 | 0.9271 |
| 0.0001 | 24.0 | 744 | 0.2022 | 0.9522 |
| 0.0002 | 25.0 | 775 | 0.2699 | 0.9339 |
| 0.0007 | 26.0 | 806 | 0.2603 | 0.9385 |
| 0.0005 | 27.0 | 837 | 0.4120 | 0.9134 |
| 0.0003 | 28.0 | 868 | 0.3550 | 0.9203 |
| 0.0008 | 29.0 | 899 | 0.3657 | 0.9203 |
| 0.0 | 30.0 | 930 | 0.3605 | 0.9203 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
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