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# physiotheraphy-E2

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.
It achieves the following results on the evaluation set:
- Accuracy: 0.9564
- F1: 0.9548
- Precision: 0.9549
- Recall: 0.9556
- Loss: 0.2235
- Classification Report:               precision    recall  f1-score   support

           0       0.92      0.95      0.93        57
           1       0.99      0.97      0.98        70
           2       1.00      1.00      1.00        33
           3       0.98      1.00      0.99        43
           4       1.00      1.00      1.00        34
           5       0.94      1.00      0.97        32
           6       0.95      0.94      0.95        65
           7       0.87      0.79      0.83        33

    accuracy                           0.96       367
   macro avg       0.95      0.96      0.95       367
weighted avg       0.96      0.96      0.96       367

- Confusion Matrix: [[0.9473684210526315, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05263157894736842], [0.0, 0.9714285714285714, 0.0, 0.0, 0.0, 0.02857142857142857, 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, 1.0, 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, 0.0, 0.0, 1.0, 0.0, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9384615384615385, 0.015384615384615385], [0.06060606060606061, 0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.09090909090909091, 0.7878787878787878]]

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Accuracy | F1     | Precision | Recall | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | Confusion Matrix                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.9195        | 0.9973 | 182  | 0.7248   | 0.7148 | 0.7616    | 0.7319 | 0.8807          |               precision    recall  f1-score   support

           0       1.00      0.51      0.67        57
           1       0.98      0.69      0.81        70
           2       0.70      0.79      0.74        33
           3       0.74      0.86      0.80        43
           4       0.45      1.00      0.62        34
           5       0.80      0.50      0.62        32
           6       0.73      0.82      0.77        65
           7       0.70      0.70      0.70        33

    accuracy                           0.72       367
   macro avg       0.76      0.73      0.71       367
weighted avg       0.79      0.72      0.73       367
 | [[0.5087719298245614, 0.017543859649122806, 0.08771929824561403, 0.08771929824561403, 0.07017543859649122, 0.0, 0.17543859649122806, 0.05263157894736842], [0.0, 0.6857142857142857, 0.0, 0.08571428571428572, 0.1, 0.05714285714285714, 0.07142857142857142, 0.0], [0.0, 0.0, 0.7878787878787878, 0.0, 0.21212121212121213, 0.0, 0.0, 0.0], [0.0, 0.0, 0.023255813953488372, 0.8604651162790697, 0.09302325581395349, 0.0, 0.023255813953488372, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.03125, 0.0, 0.46875, 0.5, 0.0, 0.0], [0.0, 0.0, 0.03076923076923077, 0.015384615384615385, 0.03076923076923077, 0.0, 0.8153846153846154, 0.1076923076923077], [0.0, 0.0, 0.06060606060606061, 0.030303030303030304, 0.09090909090909091, 0.0, 0.12121212121212122, 0.696969696969697]] |
| 0.8122        | 2.0    | 365  | 0.8365   | 0.8228 | 0.8668    | 0.8177 | 0.5425          |               precision    recall  f1-score   support

           0       0.64      0.88      0.74        57
           1       0.86      0.84      0.85        70
           2       0.91      0.94      0.93        33
           3       0.88      0.98      0.92        43
           4       0.92      1.00      0.96        34
           5       1.00      0.44      0.61        32
           6       0.91      0.89      0.90        65
           7       0.83      0.58      0.68        33

    accuracy                           0.84       367
   macro avg       0.87      0.82      0.82       367
weighted avg       0.85      0.84      0.83       367
 | [[0.8771929824561403, 0.03508771929824561, 0.03508771929824561, 0.0, 0.0, 0.0, 0.03508771929824561, 0.017543859649122806], [0.05714285714285714, 0.8428571428571429, 0.0, 0.08571428571428572, 0.0, 0.0, 0.0, 0.014285714285714285], [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], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.25, 0.1875, 0.03125, 0.0, 0.09375, 0.4375, 0.0, 0.0], [0.07692307692307693, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8923076923076924, 0.03076923076923077], [0.24242424242424243, 0.06060606060606061, 0.0, 0.0, 0.0, 0.0, 0.12121212121212122, 0.5757575757575758]]                                                                                                          |
| 0.4541        | 2.9973 | 547  | 0.7929   | 0.7963 | 0.8137    | 0.8177 | 0.7462          |               precision    recall  f1-score   support

           0       0.86      0.74      0.79        57
           1       1.00      0.51      0.68        70
           2       0.91      0.91      0.91        33
           3       0.85      0.93      0.89        43
           4       0.71      1.00      0.83        34
           5       0.70      0.94      0.80        32
           6       0.69      0.91      0.78        65
           7       0.80      0.61      0.69        33

    accuracy                           0.79       367
   macro avg       0.81      0.82      0.80       367
weighted avg       0.83      0.79      0.79       367
 | [[0.7368421052631579, 0.0, 0.05263157894736842, 0.0, 0.07017543859649122, 0.03508771929824561, 0.07017543859649122, 0.03508771929824561], [0.02857142857142857, 0.5142857142857142, 0.0, 0.1, 0.05714285714285714, 0.12857142857142856, 0.17142857142857143, 0.0], [0.0, 0.0, 0.9090909090909091, 0.0, 0.06060606060606061, 0.030303030303030304, 0.0, 0.0], [0.0, 0.0, 0.0, 0.9302325581395349, 0.0, 0.0, 0.046511627906976744, 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, 0.0625, 0.9375, 0.0, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.015384615384615385, 0.0, 0.9076923076923077, 0.03076923076923077], [0.06060606060606061, 0.0, 0.0, 0.0, 0.030303030303030304, 0.030303030303030304, 0.2727272727272727, 0.6060606060606061]]                |
| 0.3103        | 4.0    | 730  | 0.8583   | 0.8611 | 0.8684    | 0.8670 | 0.4772          |               precision    recall  f1-score   support

           0       0.96      0.77      0.85        57
           1       0.96      0.74      0.84        70
           2       0.91      0.97      0.94        33
           3       0.93      0.91      0.92        43
           4       1.00      0.97      0.99        34
           5       0.78      0.97      0.86        32
           6       0.73      0.97      0.83        65
           7       0.68      0.64      0.66        33

    accuracy                           0.86       367
   macro avg       0.87      0.87      0.86       367
weighted avg       0.87      0.86      0.86       367
 | [[0.7719298245614035, 0.017543859649122806, 0.0, 0.0, 0.0, 0.017543859649122806, 0.03508771929824561, 0.15789473684210525], [0.0, 0.7428571428571429, 0.02857142857142857, 0.02857142857142857, 0.0, 0.04285714285714286, 0.15714285714285714, 0.0], [0.0, 0.030303030303030304, 0.9696969696969697, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.9069767441860465, 0.0, 0.023255813953488372, 0.06976744186046512, 0.0], [0.0, 0.0, 0.0, 0.0, 0.9705882352941176, 0.029411764705882353, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.96875, 0.03125, 0.0], [0.015384615384615385, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9692307692307692, 0.015384615384615385], [0.030303030303030304, 0.0, 0.030303030303030304, 0.030303030303030304, 0.0, 0.09090909090909091, 0.18181818181818182, 0.6363636363636364]]           |
| 0.1391        | 4.9973 | 912  | 0.9046   | 0.9055 | 0.9004    | 0.9151 | 0.4130          |               precision    recall  f1-score   support

           0       0.90      0.79      0.84        57
           1       0.96      0.91      0.93        70
           2       0.94      1.00      0.97        33
           3       0.91      1.00      0.96        43
           4       1.00      1.00      1.00        34
           5       0.88      0.94      0.91        32
           6       0.95      0.86      0.90        65
           7       0.66      0.82      0.73        33

    accuracy                           0.90       367
   macro avg       0.90      0.92      0.91       367
weighted avg       0.91      0.90      0.91       367
 | [[0.7894736842105263, 0.0, 0.03508771929824561, 0.0, 0.0, 0.0, 0.0, 0.17543859649122806], [0.0, 0.9142857142857143, 0.0, 0.02857142857142857, 0.0, 0.05714285714285714, 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, 1.0, 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, 0.03125, 0.0, 0.0, 0.0, 0.9375, 0.0, 0.03125], [0.06153846153846154, 0.015384615384615385, 0.0, 0.015384615384615385, 0.0, 0.0, 0.8615384615384616, 0.046153846153846156], [0.030303030303030304, 0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.09090909090909091, 0.8181818181818182]]                                                                                                                                                                         |
| 0.0753        | 6.0    | 1095 | 0.9401   | 0.9367 | 0.9365    | 0.9403 | 0.2873          |               precision    recall  f1-score   support

           0       0.93      0.89      0.91        57
           1       0.97      0.97      0.97        70
           2       1.00      0.97      0.98        33
           3       1.00      0.98      0.99        43
           4       0.87      1.00      0.93        34
           5       0.84      0.97      0.90        32
           6       0.95      0.92      0.94        65
           7       0.93      0.82      0.87        33

    accuracy                           0.94       367
   macro avg       0.94      0.94      0.94       367
weighted avg       0.94      0.94      0.94       367
 | [[0.8947368421052632, 0.0, 0.0, 0.0, 0.07017543859649122, 0.017543859649122806, 0.0, 0.017543859649122806], [0.0, 0.9714285714285714, 0.0, 0.0, 0.0, 0.02857142857142857, 0.0, 0.0], [0.0, 0.0, 0.9696969696969697, 0.0, 0.030303030303030304, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.9767441860465116, 0.0, 0.023255813953488372, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.03125, 0.0, 0.0, 0.0, 0.96875, 0.0, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.0, 0.015384615384615385, 0.9230769230769231, 0.015384615384615385], [0.030303030303030304, 0.030303030303030304, 0.0, 0.0, 0.0, 0.030303030303030304, 0.09090909090909091, 0.8181818181818182]]                                                                                                                          |
| 0.0178        | 6.9973 | 1277 | 0.9455   | 0.9439 | 0.9535    | 0.9374 | 0.2430          |               precision    recall  f1-score   support

           0       0.85      0.96      0.90        57
           1       0.99      0.97      0.98        70
           2       1.00      0.97      0.98        33
           3       0.98      0.98      0.98        43
           4       1.00      1.00      1.00        34
           5       0.97      0.88      0.92        32
           6       0.93      0.95      0.94        65
           7       0.93      0.79      0.85        33

    accuracy                           0.95       367
   macro avg       0.95      0.94      0.94       367
weighted avg       0.95      0.95      0.95       367
 | [[0.9649122807017544, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03508771929824561], [0.0, 0.9714285714285714, 0.0, 0.014285714285714285, 0.0, 0.014285714285714285, 0.0, 0.0], [0.030303030303030304, 0.0, 0.9696969696969697, 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], [0.0625, 0.03125, 0.0, 0.0, 0.0, 0.875, 0.03125, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9538461538461539, 0.0], [0.09090909090909091, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12121212121212122, 0.7878787878787878]]                                                                                                                                                                                                          |
| 0.0037        | 8.0    | 1460 | 0.9564   | 0.9548 | 0.9549    | 0.9556 | 0.2235          |               precision    recall  f1-score   support

           0       0.92      0.95      0.93        57
           1       0.99      0.97      0.98        70
           2       1.00      1.00      1.00        33
           3       0.98      1.00      0.99        43
           4       1.00      1.00      1.00        34
           5       0.94      1.00      0.97        32
           6       0.95      0.94      0.95        65
           7       0.87      0.79      0.83        33

    accuracy                           0.96       367
   macro avg       0.95      0.96      0.95       367
weighted avg       0.96      0.96      0.96       367
 | [[0.9473684210526315, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05263157894736842], [0.0, 0.9714285714285714, 0.0, 0.0, 0.0, 0.02857142857142857, 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, 1.0, 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, 0.0, 0.0, 1.0, 0.0, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9384615384615385, 0.015384615384615385], [0.06060606060606061, 0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.09090909090909091, 0.7878787878787878]]                                                                                                                                                                                                                                                      |
| 0.0034        | 8.9973 | 1642 | 0.9564   | 0.9548 | 0.9549    | 0.9556 | 0.2194          |               precision    recall  f1-score   support

           0       0.92      0.95      0.93        57
           1       0.99      0.97      0.98        70
           2       1.00      1.00      1.00        33
           3       0.98      1.00      0.99        43
           4       1.00      1.00      1.00        34
           5       0.94      1.00      0.97        32
           6       0.95      0.94      0.95        65
           7       0.87      0.79      0.83        33

    accuracy                           0.96       367
   macro avg       0.95      0.96      0.95       367
weighted avg       0.96      0.96      0.96       367
 | [[0.9473684210526315, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05263157894736842], [0.0, 0.9714285714285714, 0.0, 0.0, 0.0, 0.02857142857142857, 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, 1.0, 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, 0.0, 0.0, 1.0, 0.0, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9384615384615385, 0.015384615384615385], [0.06060606060606061, 0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.09090909090909091, 0.7878787878787878]]                                                                                                                                                                                                                                                      |
| 0.0027        | 9.9726 | 1820 | 0.9564   | 0.9548 | 0.9549    | 0.9556 | 0.2193          |               precision    recall  f1-score   support

           0       0.92      0.95      0.93        57
           1       0.99      0.97      0.98        70
           2       1.00      1.00      1.00        33
           3       0.98      1.00      0.99        43
           4       1.00      1.00      1.00        34
           5       0.94      1.00      0.97        32
           6       0.95      0.94      0.95        65
           7       0.87      0.79      0.83        33

    accuracy                           0.96       367
   macro avg       0.95      0.96      0.95       367
weighted avg       0.96      0.96      0.96       367
 | [[0.9473684210526315, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05263157894736842], [0.0, 0.9714285714285714, 0.0, 0.0, 0.0, 0.02857142857142857, 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, 1.0, 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, 0.0, 0.0, 1.0, 0.0, 0.0], [0.046153846153846156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9384615384615385, 0.015384615384615385], [0.06060606060606061, 0.030303030303030304, 0.0, 0.030303030303030304, 0.0, 0.0, 0.09090909090909091, 0.7878787878787878]]                                                                                                                                                                                                                                                      |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

elbove extension knee flexion neck exercise planterflexion of foot trunk extension trunk flexion wrist extension wrist flexion