chaichy/vit-setting-forschungssem

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: best-model
+  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. -->
+
+# best-model
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5533
+- Accuracy: 0.8289
+- Precision: 0.8457
+- Recall: 0.8289
+- F1: 0.8320
+- Precision Indoor: 0.6897
+- Recall Indoor: 0.8696
+- F1 Indoor: 0.7692
+- Support Indoor: 23
+- Precision Notapplicable: 0.8182
+- Recall Notapplicable: 0.6923
+- F1 Notapplicable: 0.75
+- Support Notapplicable: 13
+- Precision Outdoor: 0.9444
+- Recall Outdoor: 0.85
+- F1 Outdoor: 0.8947
+- Support Outdoor: 40
+
+## 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: 0.01
+- train_batch_size: 16
+- eval_batch_size: 8
+- 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_ratio: 0.05
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Precision Indoor | Recall Indoor | F1 Indoor | Support Indoor | Precision Notapplicable | Recall Notapplicable | F1 Notapplicable | Support Notapplicable | Precision Outdoor | Recall Outdoor | F1 Outdoor | Support Outdoor |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:----------------:|:-------------:|:---------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:---------------------:|:-----------------:|:--------------:|:----------:|:---------------:|
+| No log        | 1.0   | 19   | 0.9758          | 0.7237   | 0.8166    | 0.7237 | 0.7386 | 0.7059           | 0.5217        | 0.6       | 23             | 0.4483                  | 1.0                  | 0.6190           | 13                    | 1.0               | 0.75           | 0.8571     | 40              |
+| 0.9607        | 2.0   | 38   | 0.5533          | 0.8289   | 0.8457    | 0.8289 | 0.8320 | 0.6897           | 0.8696        | 0.7692    | 23             | 0.8182                  | 0.6923               | 0.75             | 13                    | 0.9444            | 0.85           | 0.8947     | 40              |
+
+
+### Framework versions
+
+- Transformers 4.57.6
+- Pytorch 2.9.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.2

config.json

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+{
+  "architectures": [
+    "CustomViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "dtype": "float32",
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Indoor",
+    "1": "NotApplicable",
+    "2": "Outdoor"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Indoor": 0,
+    "NotApplicable": 1,
+    "Outdoor": 2
+  },
+  "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": "4.57.6"
+}

model.safetensors

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+size 343227052

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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