Training in progress, epoch 1

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swin-tiny-patch4-window7-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: swin-tiny-patch4-window7-224-finetuned-geo-industrials1
+  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: 0.9803921568627451
+---
+
+<!-- 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. -->
+
+# swin-tiny-patch4-window7-224-finetuned-geo-industrials1
+
+This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1426
+- Accuracy: 0.9804
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 0.8   | 3    | 0.1426          | 0.9804   |
+| No log        | 1.87  | 7    | 0.0373          | 0.9804   |
+| No log        | 2.4   | 9    | 0.0328          | 0.9804   |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cpu
+- Datasets 2.14.6
+- Tokenizers 0.14.1

all_results.json

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+{
+    "epoch": 2.4,
+    "eval_accuracy": 0.9803921568627451,
+    "eval_loss": 0.14262183010578156,
+    "eval_runtime": 4.3531,
+    "eval_samples_per_second": 11.716,
+    "eval_steps_per_second": 0.459,
+    "total_flos": 2.759014777430016e+16,
+    "train_loss": 0.25441352526346844,
+    "train_runtime": 263.5301,
+    "train_samples_per_second": 5.225,
+    "train_steps_per_second": 0.034
+}

config.json

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+{
+  "_name_or_path": "microsoft/swin-tiny-patch4-window7-224",
+  "architectures": [
+    "SwinForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    6,
+    2
+  ],
+  "drop_path_rate": 0.1,
+  "embed_dim": 96,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "AnnualCrop",
+    "1": "Cement",
+    "2": "Forest",
+    "3": "Highway",
+    "4": "OilStorage",
+    "5": "Residential",
+    "6": "River"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "AnnualCrop": 0,
+    "Cement": 1,
+    "Forest": 2,
+    "Highway": 3,
+    "OilStorage": 4,
+    "Residential": 5,
+    "River": 6
+  },
+  "layer_norm_eps": 1e-05,
+  "mlp_ratio": 4.0,
+  "model_type": "swin",
+  "num_channels": 3,
+  "num_heads": [
+    3,
+    6,
+    12,
+    24
+  ],
+  "num_layers": 4,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "patch_size": 4,
+  "path_norm": true,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.0",
+  "use_absolute_embeddings": false,
+  "window_size": 7
+}

eval_results.json

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+{
+    "epoch": 2.4,
+    "eval_accuracy": 0.9803921568627451,
+    "eval_loss": 0.14262183010578156,
+    "eval_runtime": 4.3531,
+    "eval_samples_per_second": 11.716,
+    "eval_steps_per_second": 0.459
+}

model.safetensors

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preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

runs/Nov16_17-17-34_PC27LEL8/events.out.tfevents.1700173054.PC27LEL8

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runs/Nov16_17-17-34_PC27LEL8/events.out.tfevents.1700173398.PC27LEL8

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runs/Nov17_12-13-58_PC27LEL8/events.out.tfevents.1700241238.PC27LEL8

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train_results.json

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+{
+    "epoch": 2.4,
+    "total_flos": 2.759014777430016e+16,
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+    "train_runtime": 263.5301,
+    "train_samples_per_second": 5.225,
+    "train_steps_per_second": 0.034
+}

trainer_state.json

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+{
+  "best_metric": 0.9803921568627451,
+  "best_model_checkpoint": "swin-tiny-patch4-window7-224-finetuned-geo-industrials1\\checkpoint-3",
+  "epoch": 2.4,
+  "eval_steps": 500,
+  "global_step": 9,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
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+      "eval_runtime": 4.4355,
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+      "eval_steps_per_second": 0.451,
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+      "eval_accuracy": 0.9803921568627451,
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+}

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