swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_03 – AI Model by amjadfqs | AlphaNeural AI

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amjadfqs/
swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_03

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_03

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: 1.3844
Accuracy: 0.2739
F1 Score: 0.2367
Precision: 0.3487

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-08
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Precision
1.407	0.96	20	1.3971	0.2648	0.2170	0.3446
1.4124	1.98	41	1.3943	0.2665	0.2219	0.3467
1.4035	2.99	62	1.3918	0.2682	0.2255	0.3508
1.3978	4.0	83	1.3897	0.2705	0.2293	0.3512
1.3903	4.96	103	1.3880	0.2699	0.2295	0.3492
1.3978	5.98	124	1.3866	0.2733	0.2342	0.3537
1.3864	6.99	145	1.3855	0.2739	0.2362	0.3552
1.3913	8.0	166	1.3848	0.2733	0.2357	0.3507
1.3901	8.96	186	1.3844	0.2739	0.2367	0.3487
1.3921	9.64	200	1.3844	0.2739	0.2367	0.3487

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3