transformers tensorboard safetensors swinv2 image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swinv2-large-patch4-window12-192-22k license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12-192-22k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: swinv2_zindi
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# swinv2_zindi

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 zindi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6328
- eval_accuracy: 0.7434
- eval_runtime: 234.8425
- eval_samples_per_second: 16.492
- eval_steps_per_second: 0.519
- epoch: 3.0
- step: 520

## 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: 5

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
