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README (1).md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: alzheimer-image-classification-google-vit-base-patch16
+  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: 0.9261006289308176
+pipeline_tag: image-classification
+---
+
+<!-- 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. -->
+
+# alzheimer-image-classification-google-vit-base-patch16
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [Alzheimer MRI data](https://www.kaggle.com/datasets/sachinkumar413/alzheimer-mri-dataset).
+It achieves the following results on the evaluation set:
+- Loss: 0.2127
+- Accuracy: 0.9261
+
+## Model description
+
+The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
+
+Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
+
+Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
+
+By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you.
+
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.8167        | 1.0   | 715  | 0.7520          | 0.6494   |
+| 0.6264        | 2.0   | 1431 | 0.6467          | 0.7091   |
+| 0.5003        | 3.0   | 2146 | 0.5430          | 0.7594   |
+| 0.3543        | 4.0   | 2862 | 0.4372          | 0.8145   |
+| 0.3816        | 5.0   | 3577 | 0.3681          | 0.8428   |
+| 0.2055        | 6.0   | 4293 | 0.3746          | 0.8514   |
+| 0.2526        | 7.0   | 5008 | 0.2836          | 0.8907   |
+| 0.1262        | 8.0   | 5724 | 0.2798          | 0.8954   |
+| 0.1332        | 9.0   | 6439 | 0.2301          | 0.9159   |
+| 0.0702        | 9.99  | 7150 | 0.2127          | 0.9261   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1
+- Datasets 2.14.3
+- Tokenizers 0.13.3

config (1).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": "Mild_Demented",
+    "1": "Moderate_Demented",
+    "2": "Non_Demented",
+    "3": "Very_Mild_Demented"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Mild_Demented": "0",
+    "Moderate_Demented": "1",
+    "Non_Demented": "2",
+    "Very_Mild_Demented": "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": "4.31.0"
+}

gitattributes (1)

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gitignore (1)

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preprocessor_config (1).json

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+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
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