update model card README.md

README.md

@@ -14,14 +14,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 # segformer-b0-pavement
 
-This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the reannayang/FL_pavement2 dataset.
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the reannayang/FL_pavement dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2720
-- Mean Iou: 0.0236
-- Mean Accuracy: 0.1482
-- Overall Accuracy: 0.0148
-- Per Category Iou: [0.0, 0.10746568463341145, 0.00043272809097675386, 0.010282918469171976, 0.0, nan]
-- Per Category Accuracy: [nan, 0.42292490118577075, 0.0006673429074795794, 0.020989674434350843, nan, nan]
+- Loss: 0.4558
+- Mean Iou: 0.6310
+- Mean Accuracy: 0.7758
+- Overall Accuracy: 0.9687
+- Per Category Iou: [0.0, 0.9582107718835582, 0.9831802335937301, 0.0, 0.9070478290281362, 0.9376628700260592]
+- Per Category Accuracy: [nan, 0.964865692983352, 0.9920279343235298, 0.0, 0.9513585956798234, 0.9709244156080405]
 
 ## Model description
 
@@ -50,13 +50,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                   | Per Category Accuracy                                                             |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------:|
-| 0.8156        | 10.0  | 20   | 0.8301          | 0.0022   | 0.3276        | 0.0101           | [0.0, 0.010986907023667506, 0.0, 0.0, 0.0, nan]                                    | [nan, 0.9828722002635046, 0.0, 0.0, nan, nan]                                     |
-| 0.5646        | 20.0  | 40   | 0.3979          | 0.0122   | 0.0791        | 0.0062           | [0.0, 0.05714285714285714, 0.0, 0.00402931973080715, 0.0, nan]                     | [nan, 0.22924901185770752, 0.0, 0.007955763696891046, nan, nan]                   |
-| 0.3759        | 30.0  | 60   | 0.3079          | 0.0240   | 0.1624        | 0.0179           | [0.0, 0.10663000305530095, 5.05203597049611e-05, 0.013290868731990459, 0.0, nan]   | [nan, 0.4598155467720685, 8.008114889754952e-05, 0.027196298595045985, nan, nan]  |
-| 0.3209        | 40.0  | 80   | 0.2854          | 0.0241   | 0.1478        | 0.0179           | [0.0, 0.10656292286874154, 0.00022404522266648283, 0.013613268143035184, 0.0, nan] | [nan, 0.4150197628458498, 0.00034701831188938123, 0.028042656435140777, nan, nan] |
-| 0.304         | 50.0  | 100  | 0.2720          | 0.0236   | 0.1482        | 0.0148           | [0.0, 0.10746568463341145, 0.00043272809097675386, 0.010282918469171976, 0.0, nan] | [nan, 0.42292490118577075, 0.0006673429074795794, 0.020989674434350843, nan, nan] |
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                           | Per Category Accuracy                                                                      |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|
+| 1.0751        | 10.0  | 20   | 1.1494          | 0.5858   | 0.7574        | 0.9436           | [0.0, 0.9446293533289976, 0.9477727359877293, 0.0, 0.7979866954491387, 0.8246812143200128] | [nan, 0.9457026861403329, 0.951885181714315, 0.0, 0.9519892783938614, 0.93727071521693]    |
+| 0.8732        | 20.0  | 40   | 0.6625          | 0.6209   | 0.7710        | 0.9635           | [0.0, 0.9549288283268325, 0.9793625315294657, 0.0, 0.8879129036248917, 0.903449846293977]  | [nan, 0.9626825656214891, 0.9869186233102687, 0.0, 0.9430020497188206, 0.9623139192917564] |
+| 0.4736        | 30.0  | 60   | 0.5124          | 0.6302   | 0.7753        | 0.9687           | [0.0, 0.9600678403726158, 0.9813250559739467, 0.0, 0.9086066088704611, 0.9311320479537963] | [nan, 0.968414870799714, 0.990282039102918, 0.0, 0.9501760655910022, 0.9678319134099385]   |
+| 0.503         | 40.0  | 80   | 0.4726          | 0.6305   | 0.7753        | 0.9680           | [0.0, 0.9574198453934862, 0.9821685397548652, 0.0, 0.9074944966980188, 0.9358034824412265] | [nan, 0.9645337554897355, 0.9906414881189263, 0.0, 0.953329479161192, 0.9678925507079404]  |
+| 0.6762        | 50.0  | 100  | 0.4558          | 0.6310   | 0.7758        | 0.9687           | [0.0, 0.9582107718835582, 0.9831802335937301, 0.0, 0.9070478290281362, 0.9376628700260592] | [nan, 0.964865692983352, 0.9920279343235298, 0.0, 0.9513585956798234, 0.9709244156080405]  |
 
 
 ### Framework versions