transformers tensorboard safetensors swin image-classification generated_from_trainer dataset:image_folder autotrain_compatible endpoints_compatible region:us
---
tags:
- image-classification
- generated_from_trainer
datasets:
- image_folder
model-index:
- name: swin_cont
  results: []
---

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# swin_cont

This model was trained from scratch on the zindi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4766
- eval_accuracy: 0.7545
- eval_runtime: 236.8539
- eval_samples_per_second: 16.352
- eval_steps_per_second: 0.515
- epoch: 2.0
- step: 347

## 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: 50

### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
