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.gitignore

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+checkpoint-*/

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: Brain_Tumor_Classification
+  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: 0.9646761984861227
+    - name: F1
+      type: f1
+      value: 0.9646761984861227
+    - name: Recall
+      type: recall
+      value: 0.9646761984861227
+    - name: Precision
+      type: precision
+      value: 0.9646761984861227
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Brain_Tumor_Classification
+
+This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1012
+- Accuracy: 0.9647
+- F1: 0.9647
+- Recall: 0.9647
+- Precision: 0.9647
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.4856        | 0.99  | 83   | 0.3771          | 0.8444   | 0.8444 | 0.8444 | 0.8444    |
+| 0.3495        | 1.99  | 166  | 0.2608          | 0.8949   | 0.8949 | 0.8949 | 0.8949    |
+| 0.252         | 2.99  | 249  | 0.1445          | 0.9487   | 0.9487 | 0.9487 | 0.9487    |
+| 0.2364        | 3.99  | 332  | 0.1029          | 0.9588   | 0.9588 | 0.9588 | 0.9588    |
+| 0.2178        | 4.99  | 415  | 0.1012          | 0.9647   | 0.9647 | 0.9647 | 0.9647    |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1
+- Datasets 2.6.1
+- Tokenizers 0.13.1

all_results.json

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+    "train_runtime": 7560.7227,
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+    "train_steps_per_second": 0.055
+}

config.json

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+{
+  "_name_or_path": "microsoft/swin-tiny-patch4-window7-224",
+  "architectures": [
+    "SwinForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    6,
+    2
+  ],
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+  "embed_dim": 96,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "glioma_tumor",
+    "1": "meningioma_tumor",
+    "2": "no_tumor",
+    "3": "pituitary_tumor"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "glioma_tumor": 0,
+    "meningioma_tumor": 1,
+    "no_tumor": 2,
+    "pituitary_tumor": 3
+  },
+  "layer_norm_eps": 1e-05,
+  "mlp_ratio": 4.0,
+  "model_type": "swin",
+  "num_channels": 3,
+  "num_heads": [
+    3,
+    6,
+    12,
+    24
+  ],
+  "num_layers": 4,
+  "patch_size": 4,
+  "path_norm": true,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.23.1",
+  "use_absolute_embeddings": false,
+  "window_size": 7
+}

eval_results.json

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+}

preprocessor_config.json

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+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
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+    0.485,
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+  "resample": 3,
+  "size": 224
+}

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