transformers tensorboard safetensors dinov2 image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-crop-classification
  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: 0.7472190257000384
---

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# vit-base-patch16-224-in21k-finetuned-crop-classification

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: 0.6450
- Accuracy: 0.7472

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8031        | 1.0   | 183  | 0.7603          | 0.7050   |
| 0.7311        | 2.0   | 367  | 0.7047          | 0.7250   |
| 0.7144        | 3.0   | 550  | 0.6968          | 0.7211   |
| 0.6516        | 4.0   | 734  | 0.6569          | 0.7376   |
| 0.6371        | 5.0   | 917  | 0.6483          | 0.7376   |
| 0.6246        | 6.0   | 1101 | 0.6492          | 0.7365   |
| 0.5659        | 7.0   | 1284 | 0.6481          | 0.7411   |
| 0.533         | 8.0   | 1468 | 0.6450          | 0.7472   |
| 0.5416        | 9.0   | 1651 | 0.6382          | 0.7453   |
| 0.5062        | 9.97  | 1830 | 0.6395          | 0.7461   |


### Framework versions

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