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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: physiotheraphy-E2 |
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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: train |
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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: 0.9673024523160763 |
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- name: F1 |
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type: f1 |
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value: 0.9684234987255815 |
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- name: Precision |
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type: precision |
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value: 0.9707593418301198 |
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- name: Recall |
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type: recall |
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value: 0.9667053446477023 |
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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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# physiotheraphy-E2 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9673 |
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- F1: 0.9684 |
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- Precision: 0.9708 |
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- Recall: 0.9667 |
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- Loss: 0.1718 |
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- Classification Report: precision recall f1-score support |
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0 0.92 0.95 0.93 57 |
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1 0.97 0.99 0.98 70 |
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2 0.97 1.00 0.99 33 |
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3 1.00 0.95 0.98 43 |
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4 0.97 1.00 0.99 34 |
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5 1.00 0.97 0.98 32 |
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6 0.97 0.97 0.97 65 |
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7 0.97 0.91 0.94 33 |
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accuracy 0.97 367 |
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macro avg 0.97 0.97 0.97 367 |
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weighted avg 0.97 0.97 0.97 367 |
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- Confusion Matrix: [[0.9473684210526315, 0.0, 0.0, 0.0, 0.017543859649122806, 0.0, 0.017543859649122806, 0.017543859649122806], [0.014285714285714285, 0.9857142857142858, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.046511627906976744, 0.0, 0.9534883720930233, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.03125, 0.0, 0.0, 0.0, 0.0, 0.96875, 0.0, 0.0], [0.03076923076923077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9692307692307692, 0.0], [0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0, 0.030303030303030304, 0.9090909090909091]] |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Validation Loss | Classification Report | Confusion Matrix | |
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|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.9941 | 0.9973 | 182 | 0.6975 | 0.6724 | 0.7754 | 0.6769 | 0.9489 | precision recall f1-score support |
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0 0.93 0.46 0.61 57 |
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1 0.92 0.79 0.85 70 |
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2 0.80 0.48 0.60 33 |
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3 0.86 0.70 0.77 43 |
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4 0.39 1.00 0.56 34 |
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5 0.74 0.72 0.73 32 |
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6 0.66 0.94 0.77 65 |
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7 0.92 0.33 0.49 33 |
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accuracy 0.70 367 |
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macro avg 0.78 0.68 0.67 367 |
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weighted avg 0.79 0.70 0.70 367 |
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| [[0.45614035087719296, 0.0, 0.05263157894736842, 0.03508771929824561, 0.24561403508771928, 0.05263157894736842, 0.15789473684210525, 0.0], [0.0, 0.7857142857142857, 0.0, 0.04285714285714286, 0.08571428571428572, 0.0, 0.07142857142857142, 0.014285714285714285], [0.0, 0.0, 0.48484848484848486, 0.0, 0.48484848484848486, 0.030303030303030304, 0.0, 0.0], [0.0, 0.023255813953488372, 0.0, 0.6976744186046512, 0.09302325581395349, 0.023255813953488372, 0.16279069767441862, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0625, 0.0, 0.0, 0.21875, 0.71875, 0.0, 0.0], [0.015384615384615385, 0.0, 0.0, 0.0, 0.046153846153846156, 0.0, 0.9384615384615385, 0.0], [0.030303030303030304, 0.06060606060606061, 0.030303030303030304, 0.0, 0.12121212121212122, 0.09090909090909091, 0.3333333333333333, 0.3333333333333333]] | |
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| 0.6919 | 2.0 | 365 | 0.8665 | 0.8633 | 0.8600 | 0.8742 | 0.4393 | precision recall f1-score support |
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0 0.84 0.63 0.72 57 |
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1 0.86 0.93 0.89 70 |
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2 0.84 0.97 0.90 33 |
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3 1.00 0.95 0.98 43 |
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4 0.89 1.00 0.94 34 |
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5 0.85 0.91 0.88 32 |
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6 0.97 0.88 0.92 65 |
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7 0.63 0.73 0.68 33 |
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accuracy 0.87 367 |
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macro avg 0.86 0.87 0.86 367 |
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weighted avg 0.87 0.87 0.86 367 |
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| [[0.631578947368421, 0.08771929824561403, 0.08771929824561403, 0.0, 0.03508771929824561, 0.07017543859649122, 0.0, 0.08771929824561403], [0.02857142857142857, 0.9285714285714286, 0.0, 0.0, 0.014285714285714285, 0.014285714285714285, 0.0, 0.014285714285714285], [0.0, 0.0, 0.9696969696969697, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0], [0.023255813953488372, 0.0, 0.0, 0.9534883720930233, 0.0, 0.0, 0.0, 0.023255813953488372], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.90625, 0.0, 0.09375], [0.03076923076923077, 0.03076923076923077, 0.0, 0.0, 0.0, 0.0, 0.8769230769230769, 0.06153846153846154], [0.06060606060606061, 0.12121212121212122, 0.030303030303030304, 0.0, 0.0, 0.0, 0.06060606060606061, 0.7272727272727273]] | |
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| 0.4322 | 2.9973 | 547 | 0.8501 | 0.8412 | 0.8687 | 0.8387 | 0.6005 | precision recall f1-score support |
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0 0.78 0.95 0.86 57 |
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1 1.00 0.76 0.86 70 |
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2 0.96 0.76 0.85 33 |
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3 0.83 0.91 0.87 43 |
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4 0.72 1.00 0.84 34 |
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5 0.92 0.75 0.83 32 |
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6 0.82 0.95 0.88 65 |
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7 0.91 0.64 0.75 33 |
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accuracy 0.85 367 |
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macro avg 0.87 0.84 0.84 367 |
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weighted avg 0.87 0.85 0.85 367 |
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| [[0.9473684210526315, 0.0, 0.0, 0.03508771929824561, 0.0, 0.0, 0.017543859649122806, 0.0], [0.05714285714285714, 0.7571428571428571, 0.0, 0.04285714285714286, 0.05714285714285714, 0.014285714285714285, 0.04285714285714286, 0.02857142857142857], [0.030303030303030304, 0.0, 0.7575757575757576, 0.0, 0.12121212121212122, 0.030303030303030304, 0.06060606060606061, 0.0], [0.046511627906976744, 0.0, 0.0, 0.9069767441860465, 0.0, 0.0, 0.046511627906976744, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.09375, 0.0, 0.0, 0.0, 0.15625, 0.75, 0.0, 0.0], [0.015384615384615385, 0.0, 0.0, 0.03076923076923077, 0.0, 0.0, 0.9538461538461539, 0.0], [0.12121212121212122, 0.0, 0.030303030303030304, 0.030303030303030304, 0.0, 0.0, 0.18181818181818182, 0.6363636363636364]] | |
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| 0.2358 | 4.0 | 730 | 0.9401 | 0.9392 | 0.9461 | 0.9370 | 0.2496 | precision recall f1-score support |
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0 0.82 0.96 0.89 57 |
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1 1.00 0.91 0.96 70 |
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2 1.00 0.94 0.97 33 |
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3 0.95 0.98 0.97 43 |
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4 1.00 0.85 0.92 34 |
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5 0.89 1.00 0.94 32 |
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6 0.97 0.97 0.97 65 |
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7 0.94 0.88 0.91 33 |
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accuracy 0.94 367 |
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macro avg 0.95 0.94 0.94 367 |
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weighted avg 0.95 0.94 0.94 367 |
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| [[0.9649122807017544, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03508771929824561], [0.02857142857142857, 0.9142857142857143, 0.0, 0.014285714285714285, 0.0, 0.04285714285714286, 0.0, 0.0], [0.06060606060606061, 0.0, 0.9393939393939394, 0.0, 0.0, 0.0, 0.0, 0.0], [0.023255813953488372, 0.0, 0.0, 0.9767441860465116, 0.0, 0.0, 0.0, 0.0], [0.11764705882352941, 0.0, 0.0, 0.0, 0.8529411764705882, 0.029411764705882353, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.03076923076923077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9692307692307692, 0.0], [0.030303030303030304, 0.0, 0.0, 0.030303030303030304, 0.0, 0.0, 0.06060606060606061, 0.8787878787878788]] | |
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| 0.0904 | 4.9973 | 912 | 0.9401 | 0.9448 | 0.9506 | 0.9429 | 0.2831 | precision recall f1-score support |
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0 0.79 0.98 0.88 57 |
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1 0.98 0.93 0.96 70 |
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2 1.00 0.94 0.97 33 |
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3 1.00 0.95 0.98 43 |
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4 0.97 1.00 0.99 34 |
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5 0.97 0.94 0.95 32 |
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6 0.98 0.89 0.94 65 |
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7 0.91 0.91 0.91 33 |
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accuracy 0.94 367 |
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macro avg 0.95 0.94 0.94 367 |
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weighted avg 0.95 0.94 0.94 367 |
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| [[0.9824561403508771, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.017543859649122806], [0.04285714285714286, 0.9285714285714286, 0.0, 0.0, 0.0, 0.014285714285714285, 0.0, 0.014285714285714285], [0.030303030303030304, 0.0, 0.9393939393939394, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0], [0.046511627906976744, 0.0, 0.0, 0.9534883720930233, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.03125, 0.03125, 0.0, 0.0, 0.0, 0.9375, 0.0, 0.0], [0.09230769230769231, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8923076923076924, 0.015384615384615385], [0.06060606060606061, 0.0, 0.0, 0.0, 0.0, 0.0, 0.030303030303030304, 0.9090909090909091]] | |
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| 0.0313 | 6.0 | 1095 | 0.9673 | 0.9684 | 0.9708 | 0.9667 | 0.1718 | precision recall f1-score support |
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0 0.92 0.95 0.93 57 |
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1 0.97 0.99 0.98 70 |
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2 0.97 1.00 0.99 33 |
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3 1.00 0.95 0.98 43 |
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4 0.97 1.00 0.99 34 |
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5 1.00 0.97 0.98 32 |
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6 0.97 0.97 0.97 65 |
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7 0.97 0.91 0.94 33 |
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accuracy 0.97 367 |
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macro avg 0.97 0.97 0.97 367 |
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weighted avg 0.97 0.97 0.97 367 |
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| [[0.9473684210526315, 0.0, 0.0, 0.0, 0.017543859649122806, 0.0, 0.017543859649122806, 0.017543859649122806], [0.014285714285714285, 0.9857142857142858, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.046511627906976744, 0.0, 0.9534883720930233, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.03125, 0.0, 0.0, 0.0, 0.0, 0.96875, 0.0, 0.0], [0.03076923076923077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9692307692307692, 0.0], [0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0, 0.030303030303030304, 0.9090909090909091]] | |
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| 0.0047 | 6.9973 | 1277 | 0.9646 | 0.9630 | 0.9675 | 0.9604 | 0.1481 | precision recall f1-score support |
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0 0.92 0.96 0.94 57 |
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1 1.00 0.99 0.99 70 |
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2 0.97 1.00 0.99 33 |
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3 0.98 0.98 0.98 43 |
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4 0.97 1.00 0.99 34 |
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5 1.00 0.97 0.98 32 |
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6 0.94 0.97 0.95 65 |
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7 0.96 0.82 0.89 33 |
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accuracy 0.96 367 |
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macro avg 0.97 0.96 0.96 367 |
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weighted avg 0.97 0.96 0.96 367 |
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| [[0.9649122807017544, 0.0, 0.0, 0.0, 0.017543859649122806, 0.0, 0.0, 0.017543859649122806], [0.0, 0.9857142857142858, 0.0, 0.014285714285714285, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.023255813953488372, 0.0, 0.0, 0.9767441860465116, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.03125, 0.0, 0.0, 0.0, 0.0, 0.96875, 0.0, 0.0], [0.03076923076923077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9692307692307692, 0.0], [0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0, 0.12121212121212122, 0.8181818181818182]] | |
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| 0.0019 | 7.9781 | 1456 | 0.9646 | 0.9630 | 0.9675 | 0.9604 | 0.1477 | precision recall f1-score support |
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0 0.92 0.96 0.94 57 |
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1 1.00 0.99 0.99 70 |
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2 0.97 1.00 0.99 33 |
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3 0.98 0.98 0.98 43 |
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4 0.97 1.00 0.99 34 |
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5 1.00 0.97 0.98 32 |
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6 0.94 0.97 0.95 65 |
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7 0.96 0.82 0.89 33 |
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accuracy 0.96 367 |
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macro avg 0.97 0.96 0.96 367 |
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weighted avg 0.97 0.96 0.96 367 |
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| [[0.9649122807017544, 0.0, 0.0, 0.0, 0.017543859649122806, 0.0, 0.0, 0.017543859649122806], [0.0, 0.9857142857142858, 0.0, 0.014285714285714285, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.023255813953488372, 0.0, 0.0, 0.9767441860465116, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.03125, 0.0, 0.0, 0.0, 0.0, 0.96875, 0.0, 0.0], [0.03076923076923077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9692307692307692, 0.0], [0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0, 0.12121212121212122, 0.8181818181818182]] | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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