End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: ViT_B16
+  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. -->
+
+# ViT_B16
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0999
+- Accuracy: 0.9729
+- Precision: 0.9874
+- Recall: 0.9536
+- F1: 0.9702
+- Tp: 1562
+- Tn: 1890
+- Fp: 20
+- Fn: 76
+
+## 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-06
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 276
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Tp   | Tn   | Fp  | Fn  |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:|
+| 0.6533        | 0.2477 | 55   | 0.5250          | 0.8470   | 0.8296    | 0.8413 | 0.8354 | 1378 | 1627 | 283 | 260 |
+| 0.4246        | 0.4955 | 110  | 0.3119          | 0.9081   | 0.9379    | 0.8578 | 0.8960 | 1405 | 1817 | 93  | 233 |
+| 0.2834        | 0.7432 | 165  | 0.2395          | 0.9194   | 0.9033    | 0.9243 | 0.9137 | 1514 | 1748 | 162 | 124 |
+| 0.2425        | 0.9910 | 220  | 0.1882          | 0.9369   | 0.9348    | 0.9280 | 0.9314 | 1520 | 1804 | 106 | 118 |
+| 0.2127        | 1.2387 | 275  | 0.1657          | 0.9501   | 0.9551    | 0.9359 | 0.9454 | 1533 | 1838 | 72  | 105 |
+| 0.1973        | 1.4865 | 330  | 0.1446          | 0.9580   | 0.9709    | 0.9371 | 0.9537 | 1535 | 1864 | 46  | 103 |
+| 0.1943        | 1.7342 | 385  | 0.1417          | 0.9628   | 0.9772    | 0.9414 | 0.9590 | 1542 | 1874 | 36  | 96  |
+| 0.1934        | 1.9820 | 440  | 0.1173          | 0.9696   | 0.9904    | 0.9432 | 0.9662 | 1545 | 1895 | 15  | 93  |
+| 0.1671        | 2.2297 | 495  | 0.1085          | 0.9707   | 0.9968    | 0.9396 | 0.9673 | 1539 | 1905 | 5   | 99  |
+| 0.1755        | 2.4775 | 550  | 0.1140          | 0.9713   | 0.9898    | 0.9475 | 0.9682 | 1552 | 1894 | 16  | 86  |
+| 0.1836        | 2.7252 | 605  | 0.1238          | 0.9659   | 0.9720    | 0.9536 | 0.9627 | 1562 | 1865 | 45  | 76  |
+| 0.1664        | 2.9730 | 660  | 0.1199          | 0.9667   | 0.975     | 0.9524 | 0.9636 | 1560 | 1870 | 40  | 78  |
+| 0.1693        | 3.2207 | 715  | 0.1189          | 0.9679   | 0.9745    | 0.9554 | 0.9649 | 1565 | 1869 | 41  | 73  |
+| 0.1646        | 3.4685 | 770  | 0.1073          | 0.9701   | 0.9867    | 0.9481 | 0.9670 | 1553 | 1889 | 21  | 85  |
+| 0.1585        | 3.7162 | 825  | 0.1076          | 0.9687   | 0.9805    | 0.9512 | 0.9656 | 1558 | 1879 | 31  | 80  |
+| 0.1604        | 3.9640 | 880  | 0.1054          | 0.9729   | 0.9892    | 0.9518 | 0.9701 | 1559 | 1893 | 17  | 79  |
+| 0.1701        | 4.2117 | 935  | 0.1046          | 0.9704   | 0.9806    | 0.9548 | 0.9675 | 1564 | 1879 | 31  | 74  |
+| 0.1607        | 4.4595 | 990  | 0.1039          | 0.9713   | 0.9830    | 0.9542 | 0.9684 | 1563 | 1883 | 27  | 75  |
+| 0.1631        | 4.7072 | 1045 | 0.1010          | 0.9727   | 0.9873    | 0.9530 | 0.9699 | 1561 | 1890 | 20  | 77  |
+| 0.1483        | 4.9550 | 1100 | 0.0999          | 0.9729   | 0.9874    | 0.9536 | 0.9702 | 1562 | 1890 | 20  | 76  |
+
+
+### Framework versions
+
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

config.json

@@ -0,0 +1,34 @@
+{
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "dtype": "float32",
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "0",
+    "1": "1"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "pooler_act": "tanh",
+  "pooler_output_size": 768,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "transformers_version": "5.0.0",
+  "use_cache": false
+}

model.safetensors

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+oid sha256:5912f7a0ebfabf8173e5485fc372b69e1781d29b02658feadda38cf2276bf844
+size 343223968

preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "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 5137