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Armature_Defect_Detection_Resin – AI Model by Devarshi | AlphaNeural AI
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Devarshi
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Armature_Defect_Detection_Resin
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transformers
pytorch
tensorboard
swin
image-classification
generated_from_trainer
imagefolder
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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Armature_Defect_Detection_Resin
This model is a fine-tuned version of
microsoft/swin-base-patch4-window7-224-in22k
on the imagefolder dataset. It achieves the following results on the evaluation set:
Loss: 0.4978
Accuracy: 0.76
F1: 0.76
Recall: 0.76
Precision: 0.76
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Recall
Precision
No log
0.57
1
0.7205
0.44
0.44
0.44
0.44
No log
1.57
2
0.5926
0.6
0.6
0.6
0.6
No log
2.57
3
0.4978
0.76
0.76
0.76
0.76
Framework versions
Transformers 4.23.1
Pytorch 1.13.0
Datasets 2.6.1
Tokenizers 0.13.1
