Training in progress epoch 0

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_keras_callback
+model-index:
+- name: rwang5688/vit-base-patch16-224-finetuned-eurosat
+  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. -->
+
+# rwang5688/vit-base-patch16-224-finetuned-eurosat
+
+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:
+- Train Loss: 0.5015
+- Validation Loss: 0.0894
+- Validation Accuracy: 0.9730
+- Epoch: 0
+
+## 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', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Validation Accuracy | Epoch |
+|:----------:|:---------------:|:-------------------:|:-----:|
+| 0.5015     | 0.0894          | 0.9730              | 0     |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- TensorFlow 2.9.1
+- Datasets 2.4.0
+- Tokenizers 0.12.1

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": "AnnualCrop",
+    "1": "Forest",
+    "2": "HerbaceousVegetation",
+    "3": "Highway",
+    "4": "Industrial",
+    "5": "Pasture",
+    "6": "PermanentCrop",
+    "7": "Residential",
+    "8": "River",
+    "9": "SeaLake"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "AnnualCrop": 0,
+    "Forest": 1,
+    "HerbaceousVegetation": 2,
+    "Highway": 3,
+    "Industrial": 4,
+    "Pasture": 5,
+    "PermanentCrop": 6,
+    "Residential": 7,
+    "River": 8,
+    "SeaLake": 9
+  },
+  "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": "4.21.1"
+}

logs/train/events.out.tfevents.1659670657.sagemaker-base-py-ml-g4dn-16xlarge-1bdc023fdd256d2139f27d13cd48.802.0.v2

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logs/validation/events.out.tfevents.1659672279.sagemaker-base-py-ml-g4dn-16xlarge-1bdc023fdd256d2139f27d13cd48.802.1.v2

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preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "size": 224
+}

tf_model.h5

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