--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch32-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch32-384-finetuned-humid-classes-32 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- # vit-base-patch32-384-finetuned-humid-classes-32 This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co/google/vit-base-patch32-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0547 - Accuracy: 1.0 - F1 Macro: 1.0 - Precision Macro: 1.0 - Recall Macro: 1.0 - Precision Dry: 1.0 - Recall Dry: 1.0 - F1 Dry: 1.0 - Precision Firm: 1.0 - Recall Firm: 1.0 - F1 Firm: 1.0 - Precision Humid: 1.0 - Recall Humid: 1.0 - F1 Humid: 1.0 - Precision Lump: 1.0 - Recall Lump: 1.0 - F1 Lump: 1.0 - Precision Moist: 1.0 - Recall Moist: 1.0 - F1 Moist: 1.0 - Precision Rockies: 1.0 - Recall Rockies: 1.0 - F1 Rockies: 1.0 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Precision Dry | Recall Dry | F1 Dry | Precision Firm | Recall Firm | F1 Firm | Precision Humid | Recall Humid | F1 Humid | Precision Lump | Recall Lump | F1 Lump | Precision Moist | Recall Moist | F1 Moist | Precision Rockies | Recall Rockies | F1 Rockies | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------------:|:------------:|:--------:|:--------------:|:-----------:|:-------:|:---------------:|:------------:|:--------:|:-----------------:|:--------------:|:----------:| | No log | 1.0 | 3 | 1.7800 | 0.2368 | 0.1544 | 0.2569 | 0.2667 | 0.0 | 0.0 | 0.0 | 1.0 | 0.1 | 0.1818 | 0.0 | 0.0 | 0.0 | 0.3333 | 0.5 | 0.4 | 0.0 | 0.0 | 0.0 | 0.2083 | 1.0 | 0.3448 | | No log | 2.0 | 6 | 1.4470 | 0.6053 | 0.4436 | 0.4783 | 0.5417 | 0.0 | 0.0 | 0.0 | 0.8333 | 1.0 | 0.9091 | 0.4118 | 1.0 | 0.5833 | 0.0 | 0.0 | 0.0 | 1.0 | 0.25 | 0.4 | 0.625 | 1.0 | 0.7692 | | No log | 3.0 | 9 | 1.2255 | 0.5526 | 0.3879 | 0.4432 | 0.4917 | 0.0 | 0.0 | 0.0 | 0.9091 | 1.0 | 0.9524 | 0.5 | 1.0 | 0.6667 | 0.0 | 0.0 | 0.0 | 0.25 | 0.75 | 0.375 | 1.0 | 0.2 | 0.3333 | | 1.6327 | 4.0 | 12 | 0.9398 | 0.7632 | 0.6865 | 0.6746 | 0.7361 | 1.0 | 0.6667 | 0.8 | 0.9091 | 1.0 | 0.9524 | 0.5385 | 1.0 | 0.7 | 0.0 | 0.0 | 0.0 | 0.6 | 0.75 | 0.6667 | 1.0 | 1.0 | 1.0 | | 1.6327 | 5.0 | 15 | 0.6232 | 0.8947 | 0.8751 | 0.8963 | 0.875 | 1.0 | 0.6667 | 0.8 | 1.0 | 1.0 | 1.0 | 0.7778 | 1.0 | 0.875 | 1.0 | 0.8333 | 0.9091 | 0.6 | 0.75 | 0.6667 | 1.0 | 1.0 | 1.0 | | 1.6327 | 6.0 | 18 | 0.3273 | 0.9737 | 0.9651 | 0.9792 | 0.9583 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 1.0 | 1.0 | 1.0 | 0.75 | 0.8571 | 1.0 | 1.0 | 1.0 | | 0.6436 | 7.0 | 21 | 0.2521 | 0.9211 | 0.9038 | 0.9213 | 0.8972 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7778 | 1.0 | 0.875 | 1.0 | 0.8333 | 0.9091 | 0.75 | 0.75 | 0.75 | 1.0 | 0.8 | 0.8889 | | 0.6436 | 8.0 | 24 | 0.1373 | 0.9474 | 0.9167 | 0.9524 | 0.9167 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.5 | 0.6667 | 0.7143 | 1.0 | 0.8333 | | 0.6436 | 9.0 | 27 | 0.1552 | 0.9474 | 0.9287 | 0.9375 | 0.9250 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 1.0 | 1.0 | 0.75 | 0.75 | 0.75 | 1.0 | 0.8 | 0.8889 | | 0.1165 | 10.0 | 30 | 0.0547 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1165 | 11.0 | 33 | 0.0510 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1165 | 12.0 | 36 | 0.0959 | 0.9737 | 0.9630 | 0.9667 | 0.9667 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 1.0 | 0.8889 | 1.0 | 0.8 | 0.8889 | | 0.1165 | 13.0 | 39 | 0.0668 | 0.9474 | 0.9287 | 0.9375 | 0.9250 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 1.0 | 1.0 | 0.75 | 0.75 | 0.75 | 1.0 | 0.8 | 0.8889 | | 0.023 | 14.0 | 42 | 0.0748 | 0.9737 | 0.9651 | 0.9792 | 0.9583 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 1.0 | 1.0 | 1.0 | 0.75 | 0.8571 | 1.0 | 1.0 | 1.0 | | 0.023 | 15.0 | 45 | 0.0459 | 0.9737 | 0.9651 | 0.9792 | 0.9583 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 1.0 | 1.0 | 1.0 | 0.75 | 0.8571 | 1.0 | 1.0 | 1.0 | | 0.023 | 16.0 | 48 | 0.0526 | 0.9737 | 0.9630 | 0.9667 | 0.9667 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 1.0 | 0.8889 | 1.0 | 0.8 | 0.8889 | | 0.0091 | 17.0 | 51 | 0.0736 | 0.9737 | 0.9651 | 0.9792 | 0.9583 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 1.0 | 1.0 | 1.0 | 0.75 | 0.8571 | 1.0 | 1.0 | 1.0 | | 0.0091 | 18.0 | 54 | 0.0861 | 0.9474 | 0.9402 | 0.9630 | 0.9306 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7778 | 1.0 | 0.875 | 1.0 | 0.8333 | 0.9091 | 1.0 | 0.75 | 0.8571 | 1.0 | 1.0 | 1.0 | | 0.0091 | 19.0 | 57 | 0.0599 | 0.9737 | 0.9737 | 0.9792 | 0.9722 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 0.8333 | 0.9091 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0036 | 20.0 | 60 | 0.0223 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0036 | 21.0 | 63 | 0.0082 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0036 | 22.0 | 66 | 0.0048 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0036 | 23.0 | 69 | 0.0039 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0021 | 24.0 | 72 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0021 | 25.0 | 75 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0021 | 26.0 | 78 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0016 | 27.0 | 81 | 0.0051 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0016 | 28.0 | 84 | 0.0067 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0016 | 29.0 | 87 | 0.0087 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0013 | 30.0 | 90 | 0.0108 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0013 | 31.0 | 93 | 0.0129 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0013 | 32.0 | 96 | 0.0139 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0013 | 33.0 | 99 | 0.0143 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0011 | 34.0 | 102 | 0.0144 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0011 | 35.0 | 105 | 0.0143 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0011 | 36.0 | 108 | 0.0140 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 37.0 | 111 | 0.0134 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 38.0 | 114 | 0.0126 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 39.0 | 117 | 0.0118 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 40.0 | 120 | 0.0110 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 41.0 | 123 | 0.0104 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 42.0 | 126 | 0.0100 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.001 | 43.0 | 129 | 0.0098 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 44.0 | 132 | 0.0096 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 45.0 | 135 | 0.0095 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 46.0 | 138 | 0.0093 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 47.0 | 141 | 0.0092 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 48.0 | 144 | 0.0092 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 49.0 | 147 | 0.0092 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0009 | 50.0 | 150 | 0.0091 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.57.1 - Pytorch 2.9.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0