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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ditmodel
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: train
split: train
args: train
metrics:
- name: Accuracy
type: accuracy
value: 0.9425649095200629
---
<!-- 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. -->
# ditmodel
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1359
- Accuracy: 0.9426
- Weighted f1: 0.9426
- Micro f1: 0.9426
- Macro f1: 0.9386
- Weighted recall: 0.9426
- Micro recall: 0.9426
- Macro recall: 0.9404
- Weighted precision: 0.9440
- Micro precision: 0.9426
- Macro precision: 0.9382
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.3093 | 0.98 | 38 | 0.2252 | 0.8891 | 0.8879 | 0.8891 | 0.8820 | 0.8891 | 0.8891 | 0.8738 | 0.8952 | 0.8891 | 0.8994 |
| 0.2278 | 1.99 | 77 | 0.1648 | 0.9292 | 0.9292 | 0.9292 | 0.9220 | 0.9292 | 0.9292 | 0.9221 | 0.9310 | 0.9292 | 0.9241 |
| 0.2066 | 2.94 | 114 | 0.1359 | 0.9426 | 0.9426 | 0.9426 | 0.9386 | 0.9426 | 0.9426 | 0.9404 | 0.9440 | 0.9426 | 0.9382 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.15.1
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