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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_model
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: 0.7876190476190477
---
<!-- 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. -->
# Action_model
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: 0.9250
- Accuracy: 0.7876
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1285 | 0.32 | 100 | 1.0131 | 0.7743 |
| 0.7868 | 0.64 | 200 | 0.7684 | 0.7867 |
| 0.6015 | 0.96 | 300 | 0.7090 | 0.7714 |
| 0.5209 | 1.27 | 400 | 0.7650 | 0.7571 |
| 0.4536 | 1.59 | 500 | 0.7826 | 0.7419 |
| 0.4069 | 1.91 | 600 | 0.6878 | 0.7876 |
| 0.3244 | 2.23 | 700 | 0.9184 | 0.7238 |
| 0.2618 | 2.55 | 800 | 0.8178 | 0.7552 |
| 0.342 | 2.87 | 900 | 0.8192 | 0.7648 |
| 0.2778 | 3.18 | 1000 | 0.7542 | 0.7848 |
| 0.2331 | 3.5 | 1100 | 0.8133 | 0.7695 |
| 0.2426 | 3.82 | 1200 | 0.9022 | 0.7476 |
| 0.2363 | 4.14 | 1300 | 0.9009 | 0.7619 |
| 0.2143 | 4.46 | 1400 | 0.8545 | 0.7790 |
| 0.1624 | 4.78 | 1500 | 0.9543 | 0.7533 |
| 0.2302 | 5.1 | 1600 | 0.8138 | 0.78 |
| 0.1682 | 5.41 | 1700 | 0.8490 | 0.7790 |
| 0.1674 | 5.73 | 1800 | 0.9097 | 0.7724 |
| 0.1595 | 6.05 | 1900 | 1.0542 | 0.7486 |
| 0.1335 | 6.37 | 2000 | 0.8957 | 0.7876 |
| 0.1696 | 6.69 | 2100 | 0.8860 | 0.7781 |
| 0.148 | 7.01 | 2200 | 0.9529 | 0.7733 |
| 0.1281 | 7.32 | 2300 | 0.9364 | 0.7848 |
| 0.1274 | 7.64 | 2400 | 0.9252 | 0.7676 |
| 0.1585 | 7.96 | 2500 | 0.9068 | 0.7914 |
| 0.0985 | 8.28 | 2600 | 0.9400 | 0.7829 |
| 0.1211 | 8.6 | 2700 | 0.9464 | 0.7790 |
| 0.1459 | 8.92 | 2800 | 0.9800 | 0.7695 |
| 0.1221 | 9.24 | 2900 | 0.9457 | 0.78 |
| 0.1072 | 9.55 | 3000 | 0.9209 | 0.7857 |
| 0.0607 | 9.87 | 3100 | 0.9250 | 0.7876 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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