--- 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-weld-classify 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: 0.6894977168949772 --- # vit-weld-classify 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: 0.7966 - Accuracy: 0.6895 ## 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: 18 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.8686 | 0.8130 | 100 | 0.7966 | 0.6895 | | 0.6935 | 1.6260 | 200 | 1.2217 | 0.5068 | | 0.4225 | 2.4390 | 300 | 0.9592 | 0.6210 | | 0.2586 | 3.2520 | 400 | 1.3123 | 0.5936 | | 0.237 | 4.0650 | 500 | 0.8075 | 0.6986 | | 0.2658 | 4.8780 | 600 | 1.0878 | 0.6210 | | 0.1904 | 5.6911 | 700 | 1.1048 | 0.7169 | | 0.0964 | 6.5041 | 800 | 1.3602 | 0.6849 | | 0.0474 | 7.3171 | 900 | 1.1331 | 0.7671 | | 0.1179 | 8.1301 | 1000 | 1.1228 | 0.7306 | | 0.0447 | 8.9431 | 1100 | 1.2609 | 0.7397 | | 0.0043 | 9.7561 | 1200 | 1.1746 | 0.7763 | | 0.1059 | 10.5691 | 1300 | 1.1867 | 0.7763 | | 0.0026 | 11.3821 | 1400 | 1.2890 | 0.7534 | | 0.0039 | 12.1951 | 1500 | 1.3283 | 0.7580 | | 0.002 | 13.0081 | 1600 | 1.1871 | 0.7671 | | 0.0019 | 13.8211 | 1700 | 1.1643 | 0.7900 | | 0.0264 | 14.6341 | 1800 | 1.1537 | 0.7900 | | 0.0015 | 15.4472 | 1900 | 1.1821 | 0.7945 | | 0.0015 | 16.2602 | 2000 | 1.1962 | 0.7900 | | 0.0014 | 17.0732 | 2100 | 1.2036 | 0.7900 | | 0.0014 | 17.8862 | 2200 | 1.2067 | 0.7900 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1