Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-weldclassifyv4
+  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: 0.8291814946619217
+---
+
+<!-- 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. -->
+
+# vit-weldclassifyv4
+
+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: 1.0430
+- Accuracy: 0.8292
+
+## 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: 13
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|
+| 0.9281        | 0.6329  | 100  | 0.9793          | 0.5907   |
+| 0.6894        | 1.2658  | 200  | 0.7117          | 0.6868   |
+| 0.6074        | 1.8987  | 300  | 0.7031          | 0.6940   |
+| 0.5389        | 2.5316  | 400  | 0.6998          | 0.7331   |
+| 0.2922        | 3.1646  | 500  | 0.6140          | 0.7794   |
+| 0.2661        | 3.7975  | 600  | 0.8140          | 0.7117   |
+| 0.1547        | 4.4304  | 700  | 0.8582          | 0.7189   |
+| 0.1047        | 5.0633  | 800  | 0.7366          | 0.8007   |
+| 0.0672        | 5.6962  | 900  | 1.0770          | 0.7367   |
+| 0.0316        | 6.3291  | 1000 | 0.7481          | 0.8078   |
+| 0.0367        | 6.9620  | 1100 | 0.8766          | 0.7972   |
+| 0.0185        | 7.5949  | 1200 | 0.9476          | 0.8078   |
+| 0.0254        | 8.2278  | 1300 | 1.0394          | 0.7936   |
+| 0.0035        | 8.8608  | 1400 | 0.9604          | 0.8256   |
+| 0.0028        | 9.4937  | 1500 | 1.0136          | 0.8149   |
+| 0.0026        | 10.1266 | 1600 | 1.0094          | 0.8221   |
+| 0.0024        | 10.7595 | 1700 | 1.0215          | 0.8292   |
+| 0.0024        | 11.3924 | 1800 | 1.0316          | 0.8292   |
+| 0.002         | 12.0253 | 1900 | 1.0391          | 0.8292   |
+| 0.0021        | 12.6582 | 2000 | 1.0430          | 0.8292   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.19.1

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