Hugging Face's logo

RobertoSonic
/
swinv2-tiny-patch4-window8-256-dmae-humeda-DAV50

Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
swinv2-tiny-patch4-window8-256-dmae-humeda-DAV50 / README.md
RobertoSonic's picture
RobertoSonic
End of training
b82012d verified
preview code
|
raw
history blame contribute delete
3.67 kB
metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV50
    results: []

swinv2-tiny-patch4-window8-256-dmae-humeda-DAV50

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8354
  • Accuracy: 0.7273

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: 1.5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 5 1.5451 0.3864
No log 2.0 10 1.5220 0.3864
1.4177 3.0 15 1.4938 0.4205
1.4177 4.0 20 1.4111 0.4432
1.2671 5.0 25 1.2941 0.4545
1.2671 6.0 30 1.2036 0.4545
1.2671 7.0 35 1.0816 0.5114
0.9869 8.0 40 1.0452 0.5795
0.9869 9.0 45 0.9876 0.625
0.8456 10.0 50 0.9791 0.5909
0.8456 11.0 55 0.9662 0.6023
0.7126 12.0 60 0.9302 0.6364
0.7126 13.0 65 0.9379 0.625
0.7126 14.0 70 0.9036 0.6705
0.6561 15.0 75 0.8846 0.6591
0.6561 16.0 80 0.8689 0.6591
0.6367 17.0 85 0.8543 0.6591
0.6367 18.0 90 0.8342 0.6932
0.6367 19.0 95 0.8185 0.6705
0.5463 20.0 100 0.8290 0.7159
0.5463 21.0 105 0.8354 0.7273
0.5504 22.0 110 0.8160 0.7159
0.5504 23.0 115 0.8073 0.7159
0.507 24.0 120 0.8071 0.7045
0.507 25.0 125 0.8071 0.6932
0.507 26.0 130 0.8047 0.7045
0.5226 27.0 135 0.8000 0.7045
0.5226 28.0 140 0.7987 0.7159
0.5144 29.0 145 0.8000 0.7159
0.5144 30.0 150 0.8002 0.7159
0.5144 31.0 155 0.8008 0.7159
0.4862 32.0 160 0.8008 0.7159

Framework versions

  • Transformers 4.48.2
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0