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vit-base-patch16-384-finetuned-humid-classes-13

Image Classification
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-384
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: vit-base-patch16-384-finetuned-humid-classes-13
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: validation
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 1.0
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vit-base-patch16-384-finetuned-humid-classes-13
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0117
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+ - Accuracy: 1.0
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+ - F1 Macro: 1.0
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+ - Precision Macro: 1.0
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+ - Recall Macro: 1.0
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+ - Precision Dry: 1.0
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+ - Recall Dry: 1.0
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+ - F1 Dry: 1.0
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+ - Precision Firm: 1.0
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+ - Recall Firm: 1.0
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+ - F1 Firm: 1.0
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+ - Precision Humid: 1.0
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+ - Recall Humid: 1.0
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+ - F1 Humid: 1.0
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+ - Precision Lump: 1.0
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+ - Recall Lump: 1.0
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+ - F1 Lump: 1.0
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+ - Precision Rockies: 1.0
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+ - Recall Rockies: 1.0
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+ - F1 Rockies: 1.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | 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 Rockies | Recall Rockies | F1 Rockies |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------------:|:------------:|:--------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|
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+ | No log | 1.0 | 5 | 1.5040 | 0.3387 | 0.2863 | 0.5885 | 0.3848 | 1.0 | 0.2 | 0.3333 | 0.4062 | 0.9286 | 0.5652 | 0.1364 | 0.6 | 0.2222 | 1.0 | 0.0526 | 0.1 | 0.4 | 0.1429 | 0.2105 |
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+ | 1.4809 | 2.0 | 10 | 1.1305 | 0.6613 | 0.4908 | 0.4555 | 0.5600 | 0.8889 | 0.8 | 0.8421 | 0.875 | 1.0 | 0.9333 | 0.0 | 0.0 | 0.0 | 0.5135 | 1.0 | 0.6786 | 0.0 | 0.0 | 0.0 |
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+ | 1.4809 | 3.0 | 15 | 0.7686 | 0.7903 | 0.6533 | 0.6844 | 0.6857 | 0.8333 | 1.0 | 0.9091 | 0.9333 | 1.0 | 0.9655 | 0.0 | 0.0 | 0.0 | 0.6552 | 1.0 | 0.7917 | 1.0 | 0.4286 | 0.6 |
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+ | 0.6702 | 4.0 | 20 | 0.3308 | 0.9032 | 0.7653 | 0.752 | 0.7857 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.76 | 1.0 | 0.8636 | 1.0 | 0.9286 | 0.9630 |
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+ | 0.6702 | 5.0 | 25 | 0.1514 | 0.9677 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 |
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+ | 0.204 | 6.0 | 30 | 0.1756 | 0.9516 | 0.9280 | 0.9727 | 0.9057 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.8636 | 1.0 | 0.9268 | 1.0 | 0.9286 | 0.9630 |
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+ | 0.204 | 7.0 | 35 | 0.1464 | 0.9355 | 0.9235 | 0.9247 | 0.9284 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8 | 0.8 | 1.0 | 0.8421 | 0.9143 | 0.8235 | 1.0 | 0.9032 |
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+ | 0.0809 | 8.0 | 40 | 0.0892 | 0.9839 | 0.9744 | 0.9667 | 0.9857 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8333 | 1.0 | 0.9091 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9286 | 0.9630 |
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+ | 0.0809 | 9.0 | 45 | 0.1692 | 0.9355 | 0.9156 | 0.9652 | 0.8914 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.8261 | 1.0 | 0.9048 | 1.0 | 0.8571 | 0.9231 |
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+ | 0.068 | 10.0 | 50 | 0.1271 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.068 | 11.0 | 55 | 0.2046 | 0.9194 | 0.8756 | 0.9583 | 0.8514 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.7917 | 1.0 | 0.8837 | 1.0 | 0.8571 | 0.9231 |
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+ | 0.0383 | 12.0 | 60 | 0.0567 | 0.9677 | 0.9621 | 0.9524 | 0.9752 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8333 | 1.0 | 0.9091 | 1.0 | 0.9474 | 0.9730 | 0.9286 | 0.9286 | 0.9286 |
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+ | 0.0383 | 13.0 | 65 | 0.2568 | 0.9032 | 0.8634 | 0.9432 | 0.8409 | 1.0 | 1.0 | 1.0 | 0.9333 | 1.0 | 0.9655 | 1.0 | 0.4 | 0.5714 | 0.7826 | 0.9474 | 0.8571 | 1.0 | 0.8571 | 0.9231 |
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+ | 0.0365 | 14.0 | 70 | 0.1315 | 0.9516 | 0.9353 | 0.925 | 0.9647 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.625 | 1.0 | 0.7692 | 1.0 | 0.8947 | 0.9444 | 1.0 | 0.9286 | 0.9630 |
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+ | 0.0365 | 15.0 | 75 | 0.1447 | 0.9677 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 |
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+ | 0.027 | 16.0 | 80 | 0.1043 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.027 | 17.0 | 85 | 0.0157 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.026 | 18.0 | 90 | 0.0247 | 0.9839 | 0.9726 | 0.99 | 0.96 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8889 | 0.95 | 1.0 | 0.9744 | 1.0 | 1.0 | 1.0 |
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+ | 0.026 | 19.0 | 95 | 0.1236 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.008 | 20.0 | 100 | 0.0419 | 0.9839 | 0.9726 | 0.99 | 0.96 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8889 | 0.95 | 1.0 | 0.9744 | 1.0 | 1.0 | 1.0 |
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+ | 0.008 | 21.0 | 105 | 0.0141 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0069 | 22.0 | 110 | 0.0880 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0069 | 23.0 | 115 | 0.0351 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.002 | 24.0 | 120 | 0.0368 | 0.9839 | 0.9726 | 0.99 | 0.96 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8889 | 0.95 | 1.0 | 0.9744 | 1.0 | 1.0 | 1.0 |
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+ | 0.002 | 25.0 | 125 | 0.0596 | 0.9839 | 0.9726 | 0.99 | 0.96 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8889 | 0.95 | 1.0 | 0.9744 | 1.0 | 1.0 | 1.0 |
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+ | 0.0034 | 26.0 | 130 | 0.0777 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0034 | 27.0 | 135 | 0.0405 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0017 | 28.0 | 140 | 0.0516 | 0.9839 | 0.9726 | 0.99 | 0.96 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8889 | 0.95 | 1.0 | 0.9744 | 1.0 | 1.0 | 1.0 |
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+ | 0.0017 | 29.0 | 145 | 0.0420 | 0.9839 | 0.9726 | 0.99 | 0.96 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8889 | 0.95 | 1.0 | 0.9744 | 1.0 | 1.0 | 1.0 |
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+ | 0.0017 | 30.0 | 150 | 0.0147 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0017 | 31.0 | 155 | 0.0250 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0007 | 32.0 | 160 | 0.0346 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0007 | 33.0 | 165 | 0.0379 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0007 | 34.0 | 170 | 0.0330 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0007 | 35.0 | 175 | 0.0284 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0006 | 36.0 | 180 | 0.0249 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0006 | 37.0 | 185 | 0.0221 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0005 | 38.0 | 190 | 0.0198 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0005 | 39.0 | 195 | 0.0178 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0005 | 40.0 | 200 | 0.0160 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0005 | 41.0 | 205 | 0.0147 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0005 | 42.0 | 210 | 0.0139 | 0.9839 | 0.9877 | 0.9867 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9474 | 0.9730 | 0.9333 | 1.0 | 0.9655 |
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+ | 0.0005 | 43.0 | 215 | 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 |
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+ | 0.0005 | 44.0 | 220 | 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 |
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+ | 0.0005 | 45.0 | 225 | 0.0125 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 46.0 | 230 | 0.0122 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 47.0 | 235 | 0.0120 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 48.0 | 240 | 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 |
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+ | 0.0005 | 49.0 | 245 | 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 |
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+ | 0.0005 | 50.0 | 250 | 0.0117 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.56.1
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+ - Pytorch 2.9.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.0
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