metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
model-index:
  - name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_09
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9076983503534957
          - name: Precision
            type: precision
            value: 0.9184297970931635

swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_09

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: 0.2586
Accuracy: 0.9077
F1 Score: 0.9093
Precision: 0.9184
Sensitivity: 0.9071
Specificity: 0.9766

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: 1e-05
train_batch_size: 100
eval_batch_size: 100
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 400
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Precision	Sensitivity	Specificity
1.4243	0.99	19	1.2818	0.4124	0.3570	0.4910	0.4019	0.8403
1.1046	1.97	38	0.8873	0.6658	0.6584	0.7235	0.6608	0.9117
0.5232	2.96	57	0.5753	0.7671	0.7654	0.8063	0.7631	0.9395
0.3235	4.0	77	0.4476	0.8256	0.8272	0.8496	0.8228	0.9549
0.2586	4.99	96	0.3886	0.8590	0.8608	0.8764	0.8567	0.9638
0.1986	5.97	115	0.3538	0.8641	0.8663	0.8816	0.8624	0.9652
0.166	6.96	134	0.3543	0.8649	0.8668	0.8849	0.8637	0.9655
0.1345	8.0	154	0.3729	0.8586	0.8610	0.8837	0.8571	0.9640
0.1197	8.99	173	0.2879	0.8975	0.8987	0.9098	0.8961	0.9740
0.1033	9.97	192	0.2810	0.8998	0.9013	0.9128	0.8983	0.9746
0.0957	10.96	211	0.3239	0.8802	0.8818	0.8988	0.8795	0.9696
0.085	12.0	231	0.2586	0.9077	0.9093	0.9184	0.9071	0.9766
0.0769	12.99	250	0.2662	0.9018	0.9036	0.9149	0.9011	0.9751
0.0758	13.97	269	0.2830	0.8951	0.8970	0.9102	0.8945	0.9734
0.068	14.96	288	0.2757	0.8967	0.8986	0.9113	0.8960	0.9738
0.0641	16.0	308	0.2743	0.8991	0.9008	0.9136	0.8984	0.9744
0.0623	16.99	327	0.2713	0.8987	0.9001	0.9127	0.8982	0.9743
0.0542	17.97	346	0.2650	0.8987	0.9005	0.9128	0.8980	0.9743
0.0573	18.96	365	0.2709	0.8963	0.8981	0.9112	0.8957	0.9737
0.058	19.74	380	0.2778	0.8947	0.8965	0.9101	0.8942	0.9733

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3