transformers pytorch swinv2 image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swinv2-large-patch4-window12-192-22k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12-192-22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12-192-22k-finetuned-ethzurich
  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: 0.8295454545454546
---

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# swinv2-large-patch4-window12-192-22k-finetuned-ethzurich

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the Urban Resource Cadastre dataset created by Deepika Raghu, Martin Juan José Bucher, and Catherine De Wolf (https://github.com/raghudeepika/urban-resource-cadastre-repository).
It achieves the following results on the evaluation set:
- Loss: 0.6083
- Accuracy: 0.8295

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.96  | 6    | 1.2578          | 0.6364   |
| 1.6142        | 1.92  | 12   | 0.7696          | 0.75     |
| 1.6142        | 2.88  | 18   | 0.6083          | 0.8295   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
