Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: weather_classification_ViT
+  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: 0.9679266895761741
+    - name: Precision
+      type: precision
+      value: 0.9679235596755258
+    - name: Recall
+      type: recall
+      value: 0.9679266895761741
+    - name: F1
+      type: f1
+      value: 0.9678827379290899
+---
+
+<!-- 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. -->
+
+# weather_classification_ViT
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1268
+- Accuracy: 0.9679
+- Precision: 0.9679
+- Recall: 0.9679
+- F1: 0.9679
+- Auc: 0.9974
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
+| 0.2811        | 0.2288 | 100  | 0.3139          | 0.8958   | 0.9147    | 0.8958 | 0.8970 | 0.9903 |
+| 0.1396        | 0.4577 | 200  | 0.2454          | 0.9278   | 0.9307    | 0.9278 | 0.9282 | 0.9919 |
+| 0.3761        | 0.6865 | 300  | 0.2952          | 0.9072   | 0.9117    | 0.9072 | 0.9071 | 0.9889 |
+| 0.2365        | 0.9153 | 400  | 0.1797          | 0.9444   | 0.9447    | 0.9444 | 0.9445 | 0.9940 |
+| 0.2528        | 1.1442 | 500  | 0.2470          | 0.9278   | 0.9307    | 0.9278 | 0.9278 | 0.9924 |
+| 0.2364        | 1.3730 | 600  | 0.2448          | 0.9261   | 0.9306    | 0.9261 | 0.9264 | 0.9934 |
+| 0.34          | 1.6018 | 700  | 0.1986          | 0.9404   | 0.9409    | 0.9404 | 0.9405 | 0.9929 |
+| 0.2001        | 1.8307 | 800  | 0.1525          | 0.9542   | 0.9548    | 0.9542 | 0.9539 | 0.9960 |
+| 0.0958        | 2.0595 | 900  | 0.1783          | 0.9507   | 0.9515    | 0.9507 | 0.9505 | 0.9952 |
+| 0.1862        | 2.2883 | 1000 | 0.1654          | 0.9553   | 0.9558    | 0.9553 | 0.9551 | 0.9952 |
+| 0.1021        | 2.5172 | 1100 | 0.1654          | 0.9462   | 0.9472    | 0.9462 | 0.9459 | 0.9958 |
+| 0.1178        | 2.7460 | 1200 | 0.1591          | 0.9525   | 0.9536    | 0.9525 | 0.9523 | 0.9960 |
+| 0.0474        | 2.9748 | 1300 | 0.1299          | 0.9633   | 0.9635    | 0.9633 | 0.9633 | 0.9975 |
+| 0.046         | 3.2037 | 1400 | 0.1384          | 0.9628   | 0.9628    | 0.9628 | 0.9627 | 0.9972 |
+| 0.0294        | 3.4325 | 1500 | 0.1388          | 0.9645   | 0.9644    | 0.9645 | 0.9644 | 0.9969 |
+| 0.1833        | 3.6613 | 1600 | 0.1346          | 0.9633   | 0.9634    | 0.9633 | 0.9633 | 0.9971 |
+| 0.0548        | 3.8902 | 1700 | 0.1268          | 0.9679   | 0.9679    | 0.9679 | 0.9679 | 0.9974 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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": "Clear",
+    "1": "Fog",
+    "2": "Rain",
+    "3": "Sand storm",
+    "4": "Snow"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Clear": "0",
+    "Fog": "1",
+    "Rain": "2",
+    "Sand storm": "3",
+    "Snow": "4"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2"
+}

model.safetensors

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+size 343233204

preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTFeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

runs/Jul11_06-59-42_c678d70e5ef6/events.out.tfevents.1720681198.c678d70e5ef6.578.0

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+size 50760

training_args.bin

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