license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: physiotheraphy-E2
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: 0.9673024523160763
- name: F1
type: f1
value: 0.9684234987255815
- name: Precision
type: precision
value: 0.9707593418301198
- name: Recall
type: recall
value: 0.9667053446477023
physiotheraphy-E2
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
Accuracy: 0.9673
F1: 0.9684
Precision: 0.9708
Recall: 0.9667
Loss: 0.1718
Classification Report: precision recall f1-score support
0 0.92 0.95 0.93 57 1 0.97 0.99 0.98 70 2 0.97 1.00 0.99 33 3 1.00 0.95 0.98 43 4 0.97 1.00 0.99 34 5 1.00 0.97 0.98 32 6 0.97 0.97 0.97 65 7 0.97 0.91 0.94 33accuracy 0.97 367 macro avg 0.97 0.97 0.97 367
weighted avg 0.97 0.97 0.97 367
- 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]]
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: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Validation Loss | Classification Report | Confusion Matrix |
|---|---|---|---|---|---|---|---|---|---|
| 0.9941 | 0.9973 | 182 | 0.6975 | 0.6724 | 0.7754 | 0.6769 | 0.9489 | precision recall f1-score support |
0 0.93 0.46 0.61 57
1 0.92 0.79 0.85 70
2 0.80 0.48 0.60 33
3 0.86 0.70 0.77 43
4 0.39 1.00 0.56 34
5 0.74 0.72 0.73 32
6 0.66 0.94 0.77 65
7 0.92 0.33 0.49 33
accuracy 0.70 367
macro avg 0.78 0.68 0.67 367 weighted avg 0.79 0.70 0.70 367 | [[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]] | | 0.6919 | 2.0 | 365 | 0.8665 | 0.8633 | 0.8600 | 0.8742 | 0.4393 | precision recall f1-score support
0 0.84 0.63 0.72 57
1 0.86 0.93 0.89 70
2 0.84 0.97 0.90 33
3 1.00 0.95 0.98 43
4 0.89 1.00 0.94 34
5 0.85 0.91 0.88 32
6 0.97 0.88 0.92 65
7 0.63 0.73 0.68 33
accuracy 0.87 367
macro avg 0.86 0.87 0.86 367 weighted avg 0.87 0.87 0.86 367 | [[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]] | | 0.4322 | 2.9973 | 547 | 0.8501 | 0.8412 | 0.8687 | 0.8387 | 0.6005 | precision recall f1-score support
0 0.78 0.95 0.86 57
1 1.00 0.76 0.86 70
2 0.96 0.76 0.85 33
3 0.83 0.91 0.87 43
4 0.72 1.00 0.84 34
5 0.92 0.75 0.83 32
6 0.82 0.95 0.88 65
7 0.91 0.64 0.75 33
accuracy 0.85 367
macro avg 0.87 0.84 0.84 367 weighted avg 0.87 0.85 0.85 367 | [[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]] | | 0.2358 | 4.0 | 730 | 0.9401 | 0.9392 | 0.9461 | 0.9370 | 0.2496 | precision recall f1-score support
0 0.82 0.96 0.89 57
1 1.00 0.91 0.96 70
2 1.00 0.94 0.97 33
3 0.95 0.98 0.97 43
4 1.00 0.85 0.92 34
5 0.89 1.00 0.94 32
6 0.97 0.97 0.97 65
7 0.94 0.88 0.91 33
accuracy 0.94 367
macro avg 0.95 0.94 0.94 367 weighted avg 0.95 0.94 0.94 367 | [[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]] | | 0.0904 | 4.9973 | 912 | 0.9401 | 0.9448 | 0.9506 | 0.9429 | 0.2831 | precision recall f1-score support
0 0.79 0.98 0.88 57
1 0.98 0.93 0.96 70
2 1.00 0.94 0.97 33
3 1.00 0.95 0.98 43
4 0.97 1.00 0.99 34
5 0.97 0.94 0.95 32
6 0.98 0.89 0.94 65
7 0.91 0.91 0.91 33
accuracy 0.94 367
macro avg 0.95 0.94 0.94 367 weighted avg 0.95 0.94 0.94 367 | [[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]] | | 0.0313 | 6.0 | 1095 | 0.9673 | 0.9684 | 0.9708 | 0.9667 | 0.1718 | precision recall f1-score support
0 0.92 0.95 0.93 57
1 0.97 0.99 0.98 70
2 0.97 1.00 0.99 33
3 1.00 0.95 0.98 43
4 0.97 1.00 0.99 34
5 1.00 0.97 0.98 32
6 0.97 0.97 0.97 65
7 0.97 0.91 0.94 33
accuracy 0.97 367
macro avg 0.97 0.97 0.97 367 weighted avg 0.97 0.97 0.97 367 | [[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]] | | 0.0047 | 6.9973 | 1277 | 0.9646 | 0.9630 | 0.9675 | 0.9604 | 0.1481 | precision recall f1-score support
0 0.92 0.96 0.94 57
1 1.00 0.99 0.99 70
2 0.97 1.00 0.99 33
3 0.98 0.98 0.98 43
4 0.97 1.00 0.99 34
5 1.00 0.97 0.98 32
6 0.94 0.97 0.95 65
7 0.96 0.82 0.89 33
accuracy 0.96 367
macro avg 0.97 0.96 0.96 367 weighted avg 0.97 0.96 0.96 367 | [[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]] | | 0.0019 | 7.9781 | 1456 | 0.9646 | 0.9630 | 0.9675 | 0.9604 | 0.1477 | precision recall f1-score support
0 0.92 0.96 0.94 57
1 1.00 0.99 0.99 70
2 0.97 1.00 0.99 33
3 0.98 0.98 0.98 43
4 0.97 1.00 0.99 34
5 1.00 0.97 0.98 32
6 0.94 0.97 0.95 65
7 0.96 0.82 0.89 33
accuracy 0.96 367
macro avg 0.97 0.96 0.96 367 weighted avg 0.97 0.96 0.96 367 | [[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]] |
Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1