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swinv2-base-patch4-window12-192-22k-finetuned-lora-ISIC-2019 – AI Model by TriDat | AlphaNeural AI
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TriDat
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swinv2-base-patch4-window12-192-22k-finetuned-lora-ISIC-2019
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transformers
pytorch
safetensors
swinv2
image-classification
generated_from_trainer
imagefolder
microsoft/swinv2-base-patch4-window12-192-22k
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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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
