End of training

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_keras_callback
+model-index:
+- name: arieg/bw_spec_cls_8_00_noise_200
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# arieg/bw_spec_cls_8_00_noise_200
+
+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:
+- Train Loss: 0.0115
+- Train Sparse Categorical Accuracy: 1.0
+- Validation Loss: 0.0109
+- Validation Sparse Categorical Accuracy: 1.0
+- Epoch: 19
+
+## 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:
+- optimizer: {'name': 'AdamWeightDecay', 'clipnorm': 1.0, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 30600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
+|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
+| 0.8708     | 0.9739                            | 0.3432          | 1.0                                    | 0     |
+| 0.2554     | 1.0                               | 0.1989          | 1.0                                    | 1     |
+| 0.1700     | 1.0                               | 0.1456          | 1.0                                    | 2     |
+| 0.1286     | 1.0                               | 0.1130          | 1.0                                    | 3     |
+| 0.1011     | 1.0                               | 0.0900          | 1.0                                    | 4     |
+| 0.0812     | 1.0                               | 0.0729          | 1.0                                    | 5     |
+| 0.0664     | 1.0                               | 0.0602          | 1.0                                    | 6     |
+| 0.0551     | 1.0                               | 0.0503          | 1.0                                    | 7     |
+| 0.0463     | 1.0                               | 0.0425          | 1.0                                    | 8     |
+| 0.0394     | 1.0                               | 0.0363          | 1.0                                    | 9     |
+| 0.0338     | 1.0                               | 0.0314          | 1.0                                    | 10    |
+| 0.0293     | 1.0                               | 0.0273          | 1.0                                    | 11    |
+| 0.0255     | 1.0                               | 0.0239          | 1.0                                    | 12    |
+| 0.0224     | 1.0                               | 0.0210          | 1.0                                    | 13    |
+| 0.0198     | 1.0                               | 0.0187          | 1.0                                    | 14    |
+| 0.0176     | 1.0                               | 0.0166          | 1.0                                    | 15    |
+| 0.0157     | 1.0                               | 0.0149          | 1.0                                    | 16    |
+| 0.0141     | 1.0                               | 0.0134          | 1.0                                    | 17    |
+| 0.0127     | 1.0                               | 0.0121          | 1.0                                    | 18    |
+| 0.0115     | 1.0                               | 0.0109          | 1.0                                    | 19    |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- TensorFlow 2.14.0
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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": "10",
+    "1": "140",
+    "2": "2",
+    "3": "5",
+    "4": "141",
+    "5": "190",
+    "6": "193",
+    "7": "194"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "10": "0",
+    "140": "1",
+    "141": "4",
+    "190": "5",
+    "193": "6",
+    "194": "7",
+    "2": "2",
+    "5": "3"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "qkv_bias": true,
+  "transformers_version": "4.35.0"
+}

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
+  }
+}

tf_model.h5

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+oid sha256:9200382b9d208e62952c233acbdc063f7ff23be4c05f82b38b17eae9d0ef0108
+size 343488184