End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: ArtiSikhwal/headlight_11_12_2024_google_vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: headlight_12_12_2024_google_vit-base-patch16-224-in21k
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9014772078868953
+---
+
+<!-- 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. -->
+
+# headlight_12_12_2024_google_vit-base-patch16-224-in21k
+
+This model is a fine-tuned version of [ArtiSikhwal/headlight_11_12_2024_google_vit-base-patch16-224-in21k](https://huggingface.co/ArtiSikhwal/headlight_11_12_2024_google_vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2587
+- Accuracy: 0.9015
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 512
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| No log        | 0.9995 | 492  | 0.2682          | 0.8973   |
+| 0.1998        | 1.9990 | 984  | 0.2701          | 0.8982   |
+| 0.1988        | 2.9985 | 1476 | 0.2708          | 0.8974   |
+| 0.1976        | 4.0    | 1969 | 0.2609          | 0.9013   |
+| 0.2131        | 4.9995 | 2461 | 0.2584          | 0.9011   |
+| 0.2169        | 5.9970 | 2952 | 0.2587          | 0.9015   |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.4.0
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json

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+{
+  "_name_or_path": "ArtiSikhwal/headlight_11_12_2024_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": "damage",
+    "1": "no-damage"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "damage": 0,
+    "no-damage": 1
+  },
+  "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": "4.46.3"
+}

model.safetensors

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+oid sha256:50d20da2ab90491c0e64aa5352683f7068913a1f4e78c24464d9c2f581c5dcb4
+size 343223968

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessorFast",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

runs/Dec12_07-07-16_45aa18c584e9/events.out.tfevents.1733987238.45aa18c584e9.23.0

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training_args.bin

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