Model save

README.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: finetuned-skinpics
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5138888888888888
+---
+
+<!-- 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. -->
+
+# finetuned-skinpics
+
+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 imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2540
+- Accuracy: 0.5139
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 7
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.121         | 0.57  | 100  | 1.1020          | 0.2569   |
+| 1.0768        | 1.15  | 200  | 1.0546          | 0.4792   |
+| 1.0532        | 1.72  | 300  | 1.0843          | 0.2917   |
+| 1.0096        | 2.3   | 400  | 1.0693          | 0.4792   |
+| 1.0716        | 2.87  | 500  | 1.0466          | 0.4931   |
+| 1.0346        | 3.45  | 600  | 1.0225          | 0.5139   |
+| 1.0232        | 4.02  | 700  | 1.0230          | 0.4931   |
+| 0.8936        | 4.6   | 800  | 1.0582          | 0.5069   |
+| 0.7125        | 5.17  | 900  | 1.0551          | 0.5139   |
+| 0.6025        | 5.75  | 1000 | 1.1525          | 0.5278   |
+| 0.4663        | 6.32  | 1100 | 1.2357          | 0.4653   |
+| 0.5007        | 6.9   | 1200 | 1.2540          | 0.5139   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.1+cu121
+- 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": "dry",
+    "1": "normal",
+    "2": "oily"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "dry": "0",
+    "normal": "1",
+    "oily": "2"
+  },
+  "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": "4.39.3"
+}

model.safetensors

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+size 343227052

preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTFeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": [
+    224,
+    224
+  ]
+}

runs/Apr06_11-30-59_615cc53a6797/events.out.tfevents.1712403078.615cc53a6797.172.0

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+size 34319

training_args.bin

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