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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_all_10_class
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Action_small_dataset
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8517382413087935
---
<!-- 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_all_10_class
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 Action_small_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4725
- Accuracy: 0.8517
## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2411 | 0.36 | 100 | 1.1517 | 0.7546 |
| 0.8932 | 0.72 | 200 | 0.7856 | 0.7975 |
| 0.6907 | 1.08 | 300 | 0.6636 | 0.8221 |
| 0.5841 | 1.43 | 400 | 0.6388 | 0.8160 |
| 0.5425 | 1.79 | 500 | 0.5871 | 0.8436 |
| 0.5929 | 2.15 | 600 | 0.5646 | 0.8211 |
| 0.4406 | 2.51 | 700 | 0.5439 | 0.8405 |
| 0.4541 | 2.87 | 800 | 0.5318 | 0.8415 |
| 0.3835 | 3.23 | 900 | 0.5225 | 0.8344 |
| 0.3924 | 3.58 | 1000 | 0.5515 | 0.8303 |
| 0.5741 | 3.94 | 1100 | 0.5519 | 0.8252 |
| 0.3991 | 4.3 | 1200 | 0.4990 | 0.8446 |
| 0.4732 | 4.66 | 1300 | 0.5336 | 0.8303 |
| 0.3324 | 5.02 | 1400 | 0.5351 | 0.8282 |
| 0.3433 | 5.38 | 1500 | 0.4725 | 0.8517 |
| 0.2187 | 5.73 | 1600 | 0.5042 | 0.8466 |
| 0.2952 | 6.09 | 1700 | 0.5240 | 0.8548 |
| 0.2687 | 6.45 | 1800 | 0.5523 | 0.8364 |
| 0.3111 | 6.81 | 1900 | 0.5304 | 0.8497 |
| 0.2431 | 7.17 | 2000 | 0.5104 | 0.8569 |
| 0.3265 | 7.53 | 2100 | 0.5085 | 0.8691 |
| 0.2595 | 7.89 | 2200 | 0.5015 | 0.8569 |
| 0.1825 | 8.24 | 2300 | 0.4920 | 0.8620 |
| 0.2602 | 8.6 | 2400 | 0.5016 | 0.8620 |
| 0.2628 | 8.96 | 2500 | 0.4746 | 0.8681 |
| 0.1024 | 9.32 | 2600 | 0.4818 | 0.8691 |
| 0.1468 | 9.68 | 2700 | 0.4765 | 0.8681 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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