Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-newly-trained
+  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: 0.961
+---
+
+<!-- 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. -->
+
+# vit-base-patch16-224-newly-trained
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2294
+- Accuracy: 0.961
+
+## 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: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.1404        | 0.14  | 10   | 1.6121          | 0.629    |
+| 1.3709        | 0.28  | 20   | 0.8628          | 0.882    |
+| 0.8772        | 0.43  | 30   | 0.5010          | 0.933    |
+| 0.6335        | 0.57  | 40   | 0.3399          | 0.941    |
+| 0.5387        | 0.71  | 50   | 0.2673          | 0.957    |
+| 0.4319        | 0.85  | 60   | 0.2386          | 0.958    |
+| 0.4591        | 0.99  | 70   | 0.2294          | 0.961    |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.1
+- Datasets 2.14.6
+- Tokenizers 0.14.1

config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Airplane",
+    "1": "Automobile",
+    "2": "Bird",
+    "3": "Cat",
+    "4": "Deer",
+    "5": "Dog",
+    "6": "Frog",
+    "7": "Horse",
+    "8": "Ship",
+    "9": "Truck"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Airplane": 0,
+    "Automobile": 1,
+    "Bird": 2,
+    "Cat": 3,
+    "Deer": 4,
+    "Dog": 5,
+    "Frog": 6,
+    "Horse": 7,
+    "Ship": 8,
+    "Truck": 9
+  },
+  "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": "4.35.0"
+}

model.safetensors

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+oid sha256:3c1d3344cb6f688bef2005b840122991e6eb416c3aed97011c88c32db46c6d00
+size 343248584

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 4600