--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - 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: 0.8093525179856115 --- # 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: 0.5265 - Accuracy: 0.8094 ## 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 | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.1126 | 0.6410 | 100 | 1.0171 | 0.5504 | | 0.8229 | 1.2821 | 200 | 0.7307 | 0.6942 | | 0.7224 | 1.9231 | 300 | 0.6399 | 0.7122 | | 0.3909 | 2.5641 | 400 | 0.5400 | 0.7734 | | 0.237 | 3.2051 | 500 | 0.6716 | 0.7626 | | 0.4056 | 3.8462 | 600 | 0.5265 | 0.8094 | | 0.1764 | 4.4872 | 700 | 0.9174 | 0.7446 | | 0.0546 | 5.1282 | 800 | 0.6644 | 0.8237 | | 0.0436 | 5.7692 | 900 | 0.6923 | 0.8345 | | 0.0661 | 6.4103 | 1000 | 0.6784 | 0.8345 | | 0.0167 | 7.0513 | 1100 | 0.7115 | 0.8309 | | 0.0744 | 7.6923 | 1200 | 0.6341 | 0.8525 | | 0.0047 | 8.3333 | 1300 | 0.6402 | 0.8597 | | 0.0039 | 8.9744 | 1400 | 0.5958 | 0.8849 | | 0.0029 | 9.6154 | 1500 | 0.6158 | 0.8885 | | 0.0027 | 10.2564 | 1600 | 0.6189 | 0.8885 | | 0.0025 | 10.8974 | 1700 | 0.6309 | 0.8885 | | 0.0024 | 11.5385 | 1800 | 0.6356 | 0.8885 | | 0.0023 | 12.1795 | 1900 | 0.6382 | 0.8885 | | 0.0023 | 12.8205 | 2000 | 0.6399 | 0.8885 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1