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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: Melanoma-Cancer-Image-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. -->
+
+# Melanoma-Cancer-Image-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: 0.1954
+- Accuracy: 0.9395
+
+## 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: 3e-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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.5451        | 0.99  | 68   | 0.2960          | 0.8936   |
+| 0.2488        | 1.99  | 137  | 0.2254          | 0.9105   |
+| 0.1986        | 3.0   | 206  | 0.1913          | 0.9282   |
+| 0.1714        | 4.0   | 275  | 0.1906          | 0.9264   |
+| 0.1576        | 4.99  | 343  | 0.1825          | 0.9323   |
+| 0.1359        | 5.99  | 412  | 0.1973          | 0.9318   |
+| 0.1193        | 7.0   | 481  | 0.1756          | 0.9368   |
+| 0.1062        | 8.0   | 550  | 0.1743          | 0.9382   |
+| 0.0983        | 8.99  | 618  | 0.1885          | 0.9395   |
+| 0.0797        | 9.99  | 687  | 0.1931          | 0.9309   |
+| 0.0698        | 11.0  | 756  | 0.1895          | 0.9359   |
+| 0.0657        | 12.0  | 825  | 0.1861          | 0.9368   |
+| 0.0587        | 12.99 | 893  | 0.1837          | 0.9414   |
+| 0.056         | 13.99 | 962  | 0.1936          | 0.9377   |
+| 0.0592        | 15.0  | 1031 | 0.1958          | 0.935    |
+| 0.0508        | 15.83 | 1088 | 0.1954          | 0.9395   |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- 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": "Benign",
+    "1": "Malignant"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Benign": "0",
+    "Malignant": "1"
+  },
+  "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": "4.38.2"
+}

model.safetensors

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+oid sha256:416d6a2619b2a44197037a064c4ae0dbfa10f0637d0c4fc2cd9c9b8ca257ff8b
+size 343223968

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