best_model.pt

README.md

@@ -18,12 +18,12 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Model Preparation Time: 0.001
-- Accuracy: 0.9683
-- F1: 0.9669
-- Iou: 0.9402
-- Contour Dice: 0.9519
-- Per Class Metrics: {0: {'f1': 0.98429, 'iou': 0.96907, 'accuracy': 0.97631, 'contour_dice': 0.98429}, 1: {'f1': 0.93449, 'iou': 0.87704, 'accuracy': 0.96872, 'contour_dice': 0.93449}, 2: {'f1': 0.42455, 'iou': 0.26948, 'accuracy': 0.99156, 'contour_dice': 0.42455}}
-- Loss: 0.5765
+- Accuracy: 0.9855
+- F1: 0.9845
+- Iou: 0.9719
+- Contour Dice: 0.9827
+- Per Class Metrics: {0: {'f1': 0.99421, 'iou': 0.98849, 'accuracy': 0.99132, 'contour_dice': 0.99421}, 1: {'f1': 0.97069, 'iou': 0.94305, 'accuracy': 0.98574, 'contour_dice': 0.97069}, 2: {'f1': 0.57177, 'iou': 0.40033, 'accuracy': 0.99387, 'contour_dice': 0.57177}}
+- Loss: 0.1455
 
 ## Model description
 
@@ -55,15 +55,25 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step | Model Preparation Time |        | Dice   | Class Metrics                                                                                                                                                                                                                                          | Validation Loss |
 |:-------------:|:------:|:----:|:----------------------:|:------:|:------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|
-| 1.0671        | 0.1001 | 513  | 0.001                  | 0.0174 | 0.3399 | {0: {'f1': 0.01146, 'iou': 0.00576, 'accuracy': 0.20842, 'contour_dice': 0.01146}, 1: {'f1': 0.10075, 'iou': 0.05305, 'accuracy': 0.14053, 'contour_dice': 0.10075}, 2: {'f1': 0.05262, 'iou': 0.02702, 'accuracy': 0.76933, 'contour_dice': 0.05262}} | 1.1976          |
-| 0.9789        | 0.2002 | 1026 | 0.001                  | 0.1266 | 0.4253 | {0: {'f1': 0.17912, 'iou': 0.09837, 'accuracy': 0.32393, 'contour_dice': 0.17912}, 1: {'f1': 0.35672, 'iou': 0.21708, 'accuracy': 0.29378, 'contour_dice': 0.35672}, 2: {'f1': 0.08294, 'iou': 0.04326, 'accuracy': 0.92794, 'contour_dice': 0.08294}} | 1.0129          |
-| 0.9142        | 0.3002 | 1539 | 0.001                  | 0.0798 | 0.3406 | {0: {'f1': 0.09007, 'iou': 0.04716, 'accuracy': 0.23534, 'contour_dice': 0.09007}, 1: {'f1': 0.30281, 'iou': 0.17842, 'accuracy': 0.21412, 'contour_dice': 0.30281}, 2: {'f1': 0.23584, 'iou': 0.13368, 'accuracy': 0.97521, 'contour_dice': 0.23584}} | 1.0183          |
-| 0.8372        | 0.4003 | 2052 | 0.001                  | 0.1395 | 0.3728 | {0: {'f1': 0.20698, 'iou': 0.11544, 'accuracy': 0.29956, 'contour_dice': 0.20698}, 1: {'f1': 0.35121, 'iou': 0.21301, 'accuracy': 0.28783, 'contour_dice': 0.35121}, 2: {'f1': 0.27184, 'iou': 0.1573, 'accuracy': 0.98612, 'contour_dice': 0.27184}}  | 0.9982          |
-| 0.7944        | 0.5004 | 2565 | 0.001                  | 0.7788 | 0.7999 | {0: {'f1': 0.90985, 'iou': 0.83461, 'accuracy': 0.87569, 'contour_dice': 0.90985}, 1: {'f1': 0.77275, 'iou': 0.62966, 'accuracy': 0.86359, 'contour_dice': 0.77275}, 2: {'f1': 0.31621, 'iou': 0.1878, 'accuracy': 0.98574, 'contour_dice': 0.31621}}  | 0.7430          |
-| 0.7582        | 0.6005 | 3078 | 0.001                  | 0.9402 | 0.9519 | {0: {'f1': 0.98429, 'iou': 0.96907, 'accuracy': 0.97631, 'contour_dice': 0.98429}, 1: {'f1': 0.93449, 'iou': 0.87704, 'accuracy': 0.96872, 'contour_dice': 0.93449}, 2: {'f1': 0.42455, 'iou': 0.26948, 'accuracy': 0.99156, 'contour_dice': 0.42455}} | 0.5765          |
-| 0.7361        | 0.7005 | 3591 | 0.001                  | 0.9085 | 0.9187 | {0: {'f1': 0.97374, 'iou': 0.94882, 'accuracy': 0.9603, 'contour_dice': 0.97374}, 1: {'f1': 0.89466, 'iou': 0.8094, 'accuracy': 0.9505, 'contour_dice': 0.89466}, 2: {'f1': 0.41467, 'iou': 0.26156, 'accuracy': 0.98929, 'contour_dice': 0.41467}}    | 0.6767          |
-| 0.6988        | 0.8006 | 4104 | 0.001                  | 0.1675 | 0.4246 | {0: {'f1': 0.24253, 'iou': 0.138, 'accuracy': 0.34598, 'contour_dice': 0.24253}, 1: {'f1': 0.40891, 'iou': 0.257, 'accuracy': 0.3392, 'contour_dice': 0.40891}, 2: {'f1': 0.34091, 'iou': 0.20548, 'accuracy': 0.98772, 'contour_dice': 0.34091}}      | 0.8428          |
-| 0.6579        | 0.9007 | 4617 | 0.001                  | 0.9375 | 0.9517 | {0: {'f1': 0.98326, 'iou': 0.96707, 'accuracy': 0.97513, 'contour_dice': 0.98326}, 1: {'f1': 0.93342, 'iou': 0.87515, 'accuracy': 0.96663, 'contour_dice': 0.93342}, 2: {'f1': 0.33019, 'iou': 0.19774, 'accuracy': 0.99099, 'contour_dice': 0.33019}} | 0.6605          |
+| 1.0553        | 0.1001 | 513  | 0.001                  | 0.2042 | 0.4026 | {0: {'f1': 0.00261, 'iou': 0.00131, 'accuracy': 0.25279, 'contour_dice': 0.00261}, 1: {'f1': 0.91168, 'iou': 0.83769, 'accuracy': 0.95417, 'contour_dice': 0.91168}, 2: {'f1': 0.0098, 'iou': 0.00492, 'accuracy': 0.27529, 'contour_dice': 0.0098}}   | 1.0723          |
+| 0.9452        | 0.2002 | 1026 | 0.001                  | 0.7779 | 0.7181 | {0: {'f1': 0.92874, 'iou': 0.86697, 'accuracy': 0.88625, 'contour_dice': 0.92874}, 1: {'f1': 0.69461, 'iou': 0.53211, 'accuracy': 0.88238, 'contour_dice': 0.69461}, 2: {'f1': 0.05695, 'iou': 0.02931, 'accuracy': 0.98261, 'contour_dice': 0.05695}} | 0.8706          |
+| 0.8477        | 0.3002 | 1539 | 0.001                  | 0.8084 | 0.7673 | {0: {'f1': 0.93867, 'iou': 0.88442, 'accuracy': 0.90292, 'contour_dice': 0.93867}, 1: {'f1': 0.74567, 'iou': 0.59448, 'accuracy': 0.89835, 'contour_dice': 0.74567}, 2: {'f1': 0.43351, 'iou': 0.27674, 'accuracy': 0.99005, 'contour_dice': 0.43351}} | 0.4034          |
+| 0.8114        | 0.4003 | 2052 | 0.001                  | 0.7795 | 0.7144 | {0: {'f1': 0.92943, 'iou': 0.86817, 'accuracy': 0.88683, 'contour_dice': 0.92943}, 1: {'f1': 0.69334, 'iou': 0.53062, 'accuracy': 0.88266, 'contour_dice': 0.69334}, 2: {'f1': 0.24887, 'iou': 0.14212, 'accuracy': 0.9898, 'contour_dice': 0.24887}}  | 0.5827          |
+| 0.7701        | 0.5004 | 2565 | 0.001                  | 0.9032 | 0.9078 | {0: {'f1': 0.97215, 'iou': 0.94582, 'accuracy': 0.95722, 'contour_dice': 0.97215}, 1: {'f1': 0.88981, 'iou': 0.8015, 'accuracy': 0.95011, 'contour_dice': 0.88981}, 2: {'f1': 0.23167, 'iou': 0.13101, 'accuracy': 0.99155, 'contour_dice': 0.23167}}  | 0.3269          |
+| 0.7462        | 0.6005 | 3078 | 0.001                  | 0.9018 | 0.9034 | {0: {'f1': 0.97101, 'iou': 0.94366, 'accuracy': 0.9554, 'contour_dice': 0.97101}, 1: {'f1': 0.88956, 'iou': 0.80109, 'accuracy': 0.95028, 'contour_dice': 0.88956}, 2: {'f1': 0.28195, 'iou': 0.16411, 'accuracy': 0.99182, 'contour_dice': 0.28195}}  | 0.3012          |
+| 0.7083        | 0.7005 | 3591 | 0.001                  | 0.8870 | 0.8875 | {0: {'f1': 0.96677, 'iou': 0.93567, 'accuracy': 0.94869, 'contour_dice': 0.96677}, 1: {'f1': 0.86981, 'iou': 0.76962, 'accuracy': 0.94193, 'contour_dice': 0.86981}, 2: {'f1': 0.08311, 'iou': 0.04336, 'accuracy': 0.99081, 'contour_dice': 0.08311}} | 0.3264          |
+| 0.6957        | 0.8006 | 4104 | 0.001                  | 0.8885 | 0.8861 | {0: {'f1': 0.96671, 'iou': 0.93556, 'accuracy': 0.94848, 'contour_dice': 0.96671}, 1: {'f1': 0.87151, 'iou': 0.77229, 'accuracy': 0.94332, 'contour_dice': 0.87151}, 2: {'f1': 0.25672, 'iou': 0.14726, 'accuracy': 0.99154, 'contour_dice': 0.25672}} | 0.3388          |
+| 0.6415        | 0.9007 | 4617 | 0.001                  | 0.9228 | 0.9283 | {0: {'f1': 0.97759, 'iou': 0.95616, 'accuracy': 0.96585, 'contour_dice': 0.97759}, 1: {'f1': 0.91553, 'iou': 0.84422, 'accuracy': 0.96085, 'contour_dice': 0.91553}, 2: {'f1': 0.45503, 'iou': 0.29452, 'accuracy': 0.99296, 'contour_dice': 0.45503}} | 0.2154          |
+| 0.66          | 1.0008 | 5130 | 0.001                  | 0.9150 | 0.9178 | {0: {'f1': 0.97496, 'iou': 0.95115, 'accuracy': 0.96161, 'contour_dice': 0.97496}, 1: {'f1': 0.90465, 'iou': 0.82589, 'accuracy': 0.95679, 'contour_dice': 0.90465}, 2: {'f1': 0.509, 'iou': 0.34138, 'accuracy': 0.99326, 'contour_dice': 0.509}}     | 0.2575          |
+| 0.627         | 1.1009 | 5643 | 0.001                  | 0.9406 | 0.9518 | {0: {'f1': 0.98469, 'iou': 0.96984, 'accuracy': 0.97676, 'contour_dice': 0.98469}, 1: {'f1': 0.93594, 'iou': 0.87959, 'accuracy': 0.96989, 'contour_dice': 0.93594}, 2: {'f1': 0.32331, 'iou': 0.19282, 'accuracy': 0.99207, 'contour_dice': 0.32331}} | 0.2241          |
+| 0.6033        | 1.2009 | 6156 | 0.001                  | 0.9191 | 0.9237 | {0: {'f1': 0.97668, 'iou': 0.95443, 'accuracy': 0.96429, 'contour_dice': 0.97668}, 1: {'f1': 0.91018, 'iou': 0.83517, 'accuracy': 0.95907, 'contour_dice': 0.91018}, 2: {'f1': 0.42756, 'iou': 0.27191, 'accuracy': 0.99276, 'contour_dice': 0.42756}} | 0.2139          |
+| 0.6268        | 1.3010 | 6669 | 0.001                  | 0.9675 | 0.9773 | {0: {'f1': 0.99254, 'iou': 0.98518, 'accuracy': 0.98877, 'contour_dice': 0.99254}, 1: {'f1': 0.96583, 'iou': 0.93391, 'accuracy': 0.98358, 'contour_dice': 0.96583}, 2: {'f1': 0.59851, 'iou': 0.42705, 'accuracy': 0.9941, 'contour_dice': 0.59851}}  | 0.1872          |
+| 0.5698        | 1.4011 | 7182 | 0.001                  | 0.9597 | 0.9709 | {0: {'f1': 0.99051, 'iou': 0.98119, 'accuracy': 0.98568, 'contour_dice': 0.99051}, 1: {'f1': 0.95785, 'iou': 0.91911, 'accuracy': 0.97981, 'contour_dice': 0.95785}, 2: {'f1': 0.46278, 'iou': 0.30105, 'accuracy': 0.99323, 'contour_dice': 0.46278}} | 0.1653          |
+| 0.5933        | 1.5012 | 7695 | 0.001                  | 0.9349 | 0.9416 | {0: {'f1': 0.98167, 'iou': 0.96399, 'accuracy': 0.97209, 'contour_dice': 0.98167}, 1: {'f1': 0.93053, 'iou': 0.87009, 'accuracy': 0.96767, 'contour_dice': 0.93053}, 2: {'f1': 0.45029, 'iou': 0.29056, 'accuracy': 0.99309, 'contour_dice': 0.45029}} | 0.1594          |
+| 0.6071        | 1.6012 | 8208 | 0.001                  | 0.9719 | 0.9827 | {0: {'f1': 0.99421, 'iou': 0.98849, 'accuracy': 0.99132, 'contour_dice': 0.99421}, 1: {'f1': 0.97069, 'iou': 0.94305, 'accuracy': 0.98574, 'contour_dice': 0.97069}, 2: {'f1': 0.57177, 'iou': 0.40033, 'accuracy': 0.99387, 'contour_dice': 0.57177}} | 0.1455          |
+| 0.5867        | 1.7013 | 8721 | 0.001                  | 0.9567 | 0.9657 | {0: {'f1': 0.9889, 'iou': 0.97805, 'accuracy': 0.98323, 'contour_dice': 0.9889}, 1: {'f1': 0.95391, 'iou': 0.91189, 'accuracy': 0.97813, 'contour_dice': 0.95391}, 2: {'f1': 0.59149, 'iou': 0.41994, 'accuracy': 0.99415, 'contour_dice': 0.59149}}   | 0.1466          |
+| 0.5937        | 1.8014 | 9234 | 0.001                  | 0.9305 | 0.9356 | {0: {'f1': 0.97999, 'iou': 0.96076, 'accuracy': 0.96946, 'contour_dice': 0.97999}, 1: {'f1': 0.92376, 'iou': 0.85832, 'accuracy': 0.96491, 'contour_dice': 0.92376}, 2: {'f1': 0.55434, 'iou': 0.38345, 'accuracy': 0.99389, 'contour_dice': 0.55434}} | 0.2816          |
+| 0.6021        | 1.9015 | 9747 | 0.001                  | 0.9137 | 0.9154 | {0: {'f1': 0.97439, 'iou': 0.95005, 'accuracy': 0.96068, 'contour_dice': 0.97439}, 1: {'f1': 0.90445, 'iou': 0.82557, 'accuracy': 0.95681, 'contour_dice': 0.90445}, 2: {'f1': 0.46077, 'iou': 0.29935, 'accuracy': 0.99314, 'contour_dice': 0.46077}} | 0.1777          |
 
 
 ### Framework versions

model.safetensors

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 version https://git-lfs.github.com/spec/v1
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+oid sha256:12db0c4ea98138fa3d2ecde1d2414b36ff23d7320fa2818fc3597b0e013711d6
 size 2188724

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:17e961eaf3a7355b900c971ef45eb6e8f4358aa83a2a12f9bb9fda92de959799
+oid sha256:3bfb62009d38347524cca0b69f3f8a36197c8284418aa4532e75ca8063df32f1
 size 5240