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---
library_name: transformers
license: apache-2.0
base_model: timm/resnet101.a1_in1k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-beans
results: []
---
<!-- 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-base-beans
This model is a fine-tuned version of [timm/resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5027
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.07 | 1.0 | 130 | 1.0683 | 0.4135 |
| 1.0523 | 2.0 | 260 | 1.0356 | 0.6241 |
| 1.0439 | 3.0 | 390 | 1.0045 | 0.6617 |
| 1.0056 | 4.0 | 520 | 0.9671 | 0.7293 |
| 0.9853 | 5.0 | 650 | 0.9245 | 0.7895 |
| 0.9581 | 6.0 | 780 | 0.8744 | 0.7820 |
| 0.9044 | 7.0 | 910 | 0.8172 | 0.7820 |
| 0.869 | 8.0 | 1040 | 0.7737 | 0.8271 |
| 0.8804 | 9.0 | 1170 | 0.7098 | 0.8271 |
| 0.7757 | 10.0 | 1300 | 0.6705 | 0.8120 |
| 0.7694 | 11.0 | 1430 | 0.6382 | 0.8571 |
| 0.7966 | 12.0 | 1560 | 0.6088 | 0.7895 |
| 0.7425 | 13.0 | 1690 | 0.5724 | 0.8496 |
| 0.7698 | 14.0 | 1820 | 0.5665 | 0.8195 |
| 0.6632 | 15.0 | 1950 | 0.5308 | 0.8571 |
| 0.6162 | 16.0 | 2080 | 0.5262 | 0.8346 |
| 0.6128 | 17.0 | 2210 | 0.5081 | 0.8421 |
| 0.685 | 18.0 | 2340 | 0.4913 | 0.8571 |
| 0.6614 | 19.0 | 2470 | 0.4937 | 0.8496 |
| 0.6934 | 20.0 | 2600 | 0.5027 | 0.8571 |
### Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.5.0+cu118
- Datasets 2.21.0
- Tokenizers 0.20.0
|