Edit model card

swinv2-base-patch4-window12-192-22k-finetuned-lora-ISIC-2019

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4329
  • Accuracy: 0.9160
  • Precision: 0.9157
  • Recall: 0.9160
  • F1: 0.9156

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: 0.001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1
  • Datasets 2.12.0
  • Tokenizers 0.13.2
Downloads last month
8
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for TriDat/swinv2-base-patch4-window12-192-22k-finetuned-lora-ISIC-2019

Finetuned
(18)
this model