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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: categorAI_img
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: 0.8378378378378378
---
<!-- 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. -->
# categorAI_img
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: 0.7080
- Accuracy: 0.8378
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9091 | 5 | 1.8872 | 0.3784 |
| 7.7979 | 1.9091 | 10 | 1.7777 | 0.6419 |
| 7.7979 | 2.9091 | 15 | 1.6224 | 0.6622 |
| 6.9519 | 3.9091 | 20 | 1.4667 | 0.6959 |
| 6.9519 | 4.9091 | 25 | 1.3353 | 0.7365 |
| 5.7562 | 5.9091 | 30 | 1.2522 | 0.7703 |
| 5.7562 | 6.9091 | 35 | 1.1617 | 0.7838 |
| 4.7446 | 7.9091 | 40 | 1.0967 | 0.7635 |
| 4.7446 | 8.9091 | 45 | 1.0362 | 0.7568 |
| 4.0655 | 9.9091 | 50 | 0.9349 | 0.8108 |
| 4.0655 | 10.9091 | 55 | 0.9393 | 0.7905 |
| 3.5041 | 11.9091 | 60 | 0.8859 | 0.7838 |
| 3.5041 | 12.9091 | 65 | 0.9039 | 0.7770 |
| 3.0788 | 13.9091 | 70 | 0.8123 | 0.8041 |
| 3.0788 | 14.9091 | 75 | 0.7946 | 0.8243 |
| 2.7461 | 15.9091 | 80 | 0.8003 | 0.8311 |
| 2.7461 | 16.9091 | 85 | 0.8101 | 0.7703 |
| 2.4988 | 17.9091 | 90 | 0.7111 | 0.8176 |
| 2.4988 | 18.9091 | 95 | 0.7439 | 0.8243 |
| 2.3122 | 19.9091 | 100 | 0.7542 | 0.7905 |
| 2.3122 | 20.9091 | 105 | 0.7323 | 0.8311 |
| 2.3408 | 21.9091 | 110 | 0.7175 | 0.8243 |
| 2.3408 | 22.9091 | 115 | 0.7652 | 0.8041 |
| 2.2846 | 23.9091 | 120 | 0.7211 | 0.8176 |
| 2.2846 | 24.9091 | 125 | 0.7080 | 0.8378 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1.post306
- Datasets 3.2.0
- Tokenizers 0.21.0
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