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# dit-large-finetuned-ind-5-balanced-1000

This model is a fine-tuned version of [microsoft/dit-large](https://huggingface.co/microsoft/dit-large) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.8791
- Recall: 0.8791
- F1: 0.8793
- Precision: 0.8828

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.4341        | 0.97  | 22   | 1.1630          | 0.5440   | 0.5440 | 0.4931 | 0.5730    |
| 1.1974        | 1.98  | 45   | 0.9154          | 0.6813   | 0.6813 | 0.6859 | 0.7048    |
| 0.9958        | 2.99  | 68   | 0.9074          | 0.6319   | 0.6319 | 0.6054 | 0.6731    |
| 0.8981        | 4.0   | 91   | 0.7783          | 0.6923   | 0.6923 | 0.6860 | 0.7189    |
| 0.7589        | 4.97  | 113  | 0.6676          | 0.7747   | 0.7747 | 0.7767 | 0.7935    |
| 0.7009        | 5.98  | 136  | 0.6902          | 0.7857   | 0.7857 | 0.7798 | 0.7915    |
| 0.6152        | 6.99  | 159  | 0.5767          | 0.8407   | 0.8407 | 0.8324 | 0.8477    |
| 0.5713        | 8.0   | 182  | 0.4507          | 0.8407   | 0.8407 | 0.8376 | 0.8402    |
| 0.4682        | 8.97  | 204  | 0.4233          | 0.8352   | 0.8352 | 0.8349 | 0.8348    |
| 0.4441        | 9.67  | 220  | 0.4134          | 0.8626   | 0.8626 | 0.8601 | 0.8634    |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "recall", "f1", "precision"], "base_model": "microsoft/dit-large", "model-index": [{"name": "dit-large-finetuned-ind-5-balanced-1000", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8791208791208791, "name": "Accuracy"}, {"type": "recall", "value": 0.8791208791208791, "name": "Recall"}, {"type": "f1", "value": 0.8793146058237266, "name": "F1"}, {"type": "precision", "value": 0.8828068147285884, "name": "Precision"}]}]}]}
transformers tensorboard safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/dit-large model-index autotrain_compatible endpoints_compatible region:us