Training in progress epoch 0

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_keras_callback
+model-index:
+- name: YKXBCi/resnet-50-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. -->
+
+# YKXBCi/resnet-50-euroSat
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 0.8487
+- Train Accuracy: 0.6969
+- Train Top-3-accuracy: 0.9168
+- Validation Loss: 0.4793
+- Validation Accuracy: 0.8274
+- Validation Top-3-accuracy: 0.9802
+- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3585, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
+- training_precision: mixed_float16
+
+### Training results
+
+| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
+|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
+| 0.8487     | 0.6969         | 0.9168               | 0.4793          | 0.8274              | 0.9802                    | 0     |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- TensorFlow 2.6.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1

config.json

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+{
+  "_name_or_path": "microsoft/resnet-50",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "encoder_stride": 16,
+  "hidden_act": "relu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
+  "id2label": {
+    "0": "River",
+    "1": "AnnualCrop",
+    "2": "HerbaceousVegetation",
+    "3": "Industrial",
+    "4": "Residential",
+    "5": "Highway",
+    "6": "Pasture",
+    "7": "Forest",
+    "8": "SeaLake",
+    "9": "PermanentCrop"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "AnnualCrop": "1",
+    "Forest": "7",
+    "HerbaceousVegetation": "2",
+    "Highway": "5",
+    "Industrial": "3",
+    "Pasture": "6",
+    "PermanentCrop": "9",
+    "Residential": "4",
+    "River": "0",
+    "SeaLake": "8"
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_type": "bottleneck",
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0"
+}

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