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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: weeds_swin_balanced
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9666666666666667
---
<!-- 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. -->
# weeds_swin_balanced
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1356
- Accuracy: 0.9667
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5298 | 1.0 | 150 | 0.3729 | 0.8633 |
| 0.4204 | 2.0 | 300 | 0.2560 | 0.8933 |
| 0.3229 | 3.0 | 450 | 0.3213 | 0.88 |
| 0.2347 | 4.0 | 600 | 0.2031 | 0.9233 |
| 0.3342 | 5.0 | 750 | 0.1912 | 0.9367 |
| 0.2106 | 6.0 | 900 | 0.1365 | 0.9467 |
| 0.1891 | 7.0 | 1050 | 0.1927 | 0.97 |
| 0.0629 | 8.0 | 1200 | 0.1726 | 0.9467 |
| 0.1169 | 9.0 | 1350 | 0.1363 | 0.9633 |
| 0.0426 | 10.0 | 1500 | 0.1356 | 0.9667 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
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