Model save

README.md

@@ -25,16 +25,16 @@ model-index:
  metrics:
  - name: Accuracy
  type: accuracy
- value: 0.9673024523160763
+ value: 0.9564032697547684
  - name: F1
  type: f1
- value: 0.9684234987255815
+ value: 0.9548484656593037
  - name: Precision
  type: precision
- value: 0.9707593418301198
+ value: 0.9548752935240721
  - name: Recall
  type: recall
- value: 0.9667053446477023
+ value: 0.9556421648526912
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,27 +44,27 @@ should probably proofread and complete it, then remove this comment. -->
 
 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: 0.9673
-- F1: 0.9684
-- Precision: 0.9708
-- Recall: 0.9667
-- Loss: 0.1718
+- 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 0.97 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
+ 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 0.97 367
- macro avg 0.97 0.97 0.97 367
-weighted avg 0.97 0.97 0.97 367
+ 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, 0.017543859649122806, 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]]
+- 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
 
@@ -92,133 +92,163 @@ The following hyperparameters were used during training:
 - 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
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Validation Loss | Classification Report | Confusion Matrix |
-|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| 0.9941 | 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
+| 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 0.95 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
+ 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 0.94 367
+ accuracy 0.95 367
  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
+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 0.97 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
+ 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 0.97 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
+ 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 0.97 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]] |
+ 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

model.safetensors

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 version https://git-lfs.github.com/spec/v1
-oid sha256:578698d6edaba2679671c2bd3e02d47844d64e68f3cf11f77383455e2e6ce14b
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 size 343242432

runs/Jul29_11-39-36_280f3bf22da3/events.out.tfevents.1722253221.280f3bf22da3.774.0

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 version https://git-lfs.github.com/spec/v1
-oid sha256:5d3a456db673dfad426e76cc0ad7114a144e0990e415bcaf973d0de9d3c00967
-size 11628
+oid sha256:18310f05c87f8596afa8fb2af1e00091ce7123be5f84ab1957bf069a3601fcf9
+size 12454

runs/Jul29_11-39-36_280f3bf22da3/events.out.tfevents.1722254278.280f3bf22da3.774.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ed6041702dfb265bed571c358a3ca034ca1c2b8853a8011f25f40bacb59b8ef
+size 560