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swinv2_zindi – AI Model by DazMashaly | AlphaNeural AI

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DazMashaly/
swinv2_zindi

like

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image-classification

generated_from_trainer

microsoft/swinv2-large-patch4-window12-192-22k

autotrain_compatible

endpoints_compatible

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swinv2_zindi

This model is a fine-tuned version of 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