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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-Soybean_11-46
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: 0.9305555555555556
---
<!-- 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-patch16-224-Soybean_11-46
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2058
- Accuracy: 0.9306
## 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: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3661 | 1.0 | 11 | 1.3698 | 0.5069 |
| 0.9979 | 2.0 | 22 | 0.9817 | 0.6632 |
| 0.6746 | 3.0 | 33 | 0.7423 | 0.7396 |
| 0.6364 | 4.0 | 44 | 0.6075 | 0.7569 |
| 0.5425 | 5.0 | 55 | 0.5500 | 0.7951 |
| 0.5001 | 6.0 | 66 | 0.4883 | 0.8160 |
| 0.3519 | 7.0 | 77 | 0.4539 | 0.8264 |
| 0.4421 | 8.0 | 88 | 0.4483 | 0.8194 |
| 0.3207 | 9.0 | 99 | 0.3785 | 0.8438 |
| 0.3682 | 10.0 | 110 | 0.3385 | 0.8646 |
| 0.2642 | 11.0 | 121 | 0.3827 | 0.8403 |
| 0.3444 | 12.0 | 132 | 0.3462 | 0.8507 |
| 0.2423 | 13.0 | 143 | 0.3170 | 0.8681 |
| 0.3168 | 14.0 | 154 | 0.3168 | 0.8715 |
| 0.2781 | 15.0 | 165 | 0.3323 | 0.8333 |
| 0.2411 | 16.0 | 176 | 0.3200 | 0.8715 |
| 0.2276 | 17.0 | 187 | 0.3296 | 0.875 |
| 0.192 | 18.0 | 198 | 0.3119 | 0.8854 |
| 0.1612 | 19.0 | 209 | 0.3647 | 0.875 |
| 0.1084 | 20.0 | 220 | 0.2641 | 0.8993 |
| 0.2099 | 21.0 | 231 | 0.2807 | 0.8958 |
| 0.1666 | 22.0 | 242 | 0.2595 | 0.9097 |
| 0.1355 | 23.0 | 253 | 0.2735 | 0.8924 |
| 0.1165 | 24.0 | 264 | 0.3238 | 0.8785 |
| 0.112 | 25.0 | 275 | 0.3066 | 0.8889 |
| 0.1191 | 26.0 | 286 | 0.2427 | 0.9062 |
| 0.1293 | 27.0 | 297 | 0.2536 | 0.9201 |
| 0.2932 | 28.0 | 308 | 0.2707 | 0.8924 |
| 0.0918 | 29.0 | 319 | 0.2688 | 0.8924 |
| 0.1529 | 30.0 | 330 | 0.2715 | 0.8889 |
| 0.227 | 31.0 | 341 | 0.2664 | 0.9028 |
| 0.1044 | 32.0 | 352 | 0.2809 | 0.8993 |
| 0.0894 | 33.0 | 363 | 0.2863 | 0.8924 |
| 0.0566 | 34.0 | 374 | 0.2474 | 0.9201 |
| 0.0915 | 35.0 | 385 | 0.2428 | 0.9097 |
| 0.1136 | 36.0 | 396 | 0.2545 | 0.9097 |
| 0.0947 | 37.0 | 407 | 0.2599 | 0.9097 |
| 0.1012 | 38.0 | 418 | 0.2454 | 0.9167 |
| 0.0465 | 39.0 | 429 | 0.2435 | 0.9201 |
| 0.0299 | 40.0 | 440 | 0.2532 | 0.9062 |
| 0.0311 | 41.0 | 451 | 0.2298 | 0.9271 |
| 0.0796 | 42.0 | 462 | 0.2422 | 0.9167 |
| 0.058 | 43.0 | 473 | 0.2058 | 0.9306 |
| 0.0853 | 44.0 | 484 | 0.2266 | 0.9306 |
| 0.0868 | 45.0 | 495 | 0.2266 | 0.9236 |
| 0.0554 | 46.0 | 506 | 0.2163 | 0.9271 |
| 0.0508 | 47.0 | 517 | 0.2104 | 0.9306 |
| 0.0589 | 48.0 | 528 | 0.2172 | 0.9271 |
| 0.0369 | 49.0 | 539 | 0.2214 | 0.9271 |
| 0.0852 | 50.0 | 550 | 0.2241 | 0.9271 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1
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