--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: delivery_truck_classification 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: 0.9814814814814815 --- # delivery_truck_classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1169 - Accuracy: 0.9815 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 0.1169 | 0.9815 | | No log | 1.8 | 6 | 0.1169 | 0.9815 | | No log | 2.8 | 9 | 0.1169 | 0.9815 | | No log | 3.8 | 12 | 0.1169 | 0.9815 | | No log | 4.8 | 15 | 0.1169 | 0.9815 | | No log | 5.8 | 18 | 0.1169 | 0.9815 | | 0.3448 | 6.8 | 21 | 0.1169 | 0.9815 | | 0.3448 | 7.8 | 24 | 0.1169 | 0.9815 | | 0.3448 | 8.8 | 27 | 0.1169 | 0.9815 | | 0.3448 | 9.8 | 30 | 0.1169 | 0.9815 | | 0.3448 | 10.8 | 33 | 0.1169 | 0.9815 | | 0.3448 | 11.8 | 36 | 0.1169 | 0.9815 | | 0.3448 | 12.8 | 39 | 0.1169 | 0.9815 | | 0.3344 | 13.8 | 42 | 0.1169 | 0.9815 | | 0.3344 | 14.8 | 45 | 0.1169 | 0.9815 | | 0.3344 | 15.8 | 48 | 0.1169 | 0.9815 | | 0.3344 | 16.8 | 51 | 0.1169 | 0.9815 | | 0.3344 | 17.8 | 54 | 0.1169 | 0.9815 | | 0.3344 | 18.8 | 57 | 0.1169 | 0.9815 | | 0.3274 | 19.8 | 60 | 0.1169 | 0.9815 | | 0.3274 | 20.8 | 63 | 0.1169 | 0.9815 | | 0.3274 | 21.8 | 66 | 0.1169 | 0.9815 | | 0.3274 | 22.8 | 69 | 0.1169 | 0.9815 | | 0.3274 | 23.8 | 72 | 0.1169 | 0.9815 | | 0.3274 | 24.8 | 75 | 0.1169 | 0.9815 | | 0.3274 | 25.8 | 78 | 0.1169 | 0.9815 | | 0.3426 | 26.8 | 81 | 0.1169 | 0.9815 | | 0.3426 | 27.8 | 84 | 0.1169 | 0.9815 | | 0.3426 | 28.8 | 87 | 0.1169 | 0.9815 | | 0.3426 | 29.8 | 90 | 0.1169 | 0.9815 | | 0.3426 | 30.8 | 93 | 0.1169 | 0.9815 | | 0.3426 | 31.8 | 96 | 0.1169 | 0.9815 | | 0.3426 | 32.8 | 99 | 0.1169 | 0.9815 | | 0.3436 | 33.8 | 102 | 0.1169 | 0.9815 | | 0.3436 | 34.8 | 105 | 0.1169 | 0.9815 | | 0.3436 | 35.8 | 108 | 0.1169 | 0.9815 | | 0.3436 | 36.8 | 111 | 0.1169 | 0.9815 | | 0.3436 | 37.8 | 114 | 0.1169 | 0.9815 | | 0.3436 | 38.8 | 117 | 0.1169 | 0.9815 | | 0.3243 | 39.8 | 120 | 0.1169 | 0.9815 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2