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README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Brain-Tumor-Classification
+  results: []
+---
+
+<!-- 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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0872
+- Accuracy: 0.9758
+
+## 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: 16
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.2074        | 1.0   | 44   | 0.8060          | 0.8128   |
+| 0.4897        | 2.0   | 88   | 0.3008          | 0.9274   |
+| 0.2462        | 3.0   | 132  | 0.2464          | 0.9331   |
+| 0.1937        | 4.0   | 176  | 0.1918          | 0.9502   |
+| 0.1523        | 5.0   | 220  | 0.1699          | 0.9502   |
+| 0.1371        | 6.0   | 264  | 0.1372          | 0.9644   |
+| 0.1104        | 7.0   | 308  | 0.1121          | 0.9708   |
+| 0.1097        | 8.0   | 352  | 0.1220          | 0.9651   |
+| 0.1015        | 9.0   | 396  | 0.1053          | 0.9737   |
+| 0.0841        | 10.0  | 440  | 0.1142          | 0.9708   |
+| 0.0839        | 11.0  | 484  | 0.1073          | 0.9708   |
+| 0.0771        | 12.0  | 528  | 0.1156          | 0.9665   |
+| 0.074         | 13.0  | 572  | 0.1203          | 0.9644   |
+| 0.0652        | 14.0  | 616  | 0.0706          | 0.9858   |
+| 0.0694        | 15.0  | 660  | 0.0984          | 0.9744   |
+| 0.0596        | 16.0  | 704  | 0.0872          | 0.9758   |
+
+
+### Framework versions
+
+- Transformers 4.38.1
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224-in21k",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "glioma",
+    "1": "meningioma",
+    "2": "notumor",
+    "3": "pituitary"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "glioma": "0",
+    "meningioma": "1",
+    "notumor": "2",
+    "pituitary": "3"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1"
+}

model.safetensors

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+oid sha256:51d06d0a5fc79184da94f8badd53f513dda7f8f5ae8d9d9a53aa55d9bd87481b
+size 343230128

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

training_args.bin

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+size 4920