Log Segformer metrics curves, class-wise parameters, and models for segformer_run_2026-06-24_11-25

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+performance_curves.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,115 +1,46 @@
 ---
-library_name: transformers
-license: other
-base_model: nvidia/mit-b0
+language: en
+license: mit
 tags:
-- generated_from_trainer
+- semantic-segmentation
+- pytorch
+- transformers
+- segformer
+- agricultural-cv
 model-index:
-- name: corm-fusarium-segformer-mit-b0
-  results: []
+- name: segformer_run_2026-06-24_11-25
+  results:
+  - task:
+      type: semantic-segmentation
+      name: Corm Segmentation
+    metrics:
+    - type: mean_iou
+      value: 0.9278693459984201
+      name: Best Val Mean IoU
+    - type: damage_iou
+      value: 0.8253293380141987
+      name: Best Val Damage-Class IoU
+    - type: corm_iou
+      value: 0.9605193726057429
+      name: Best Val Corm-Class IoU
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Segformer Corm & Damage Segmentation Model
 
-# corm-fusarium-segformer-mit-b0
+This repository contains weights and comprehensive logs for experiment `segformer_run_2026-06-24_11-25`.
 
-This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0123
-- Mean Iou: 0.9276
-- Mean Accuracy: 0.9628
-- Bg Iou: 0.9978
-- Damage Iou: 0.8250
-- Corm Iou: 0.9600
-- Bg Accuracy: 0.9991
-- Damage Accuracy: 0.9120
-- Corm Accuracy: 0.9773
+## Model Summary
+- **Architecture Type:** Segformer (`nvidia/mit-b0`)
+- **Input Channels:** RGB
+- **Classes:** - Class 0: Background
+  - Class 1: Damage
+  - Class 2: Corm
 
-## Model description
+## Best Metric Deliverables
+- **Validation Mean IoU:** `0.9278693459984201`
+- **Validation Damage IoU:** `0.8253293380141987`
+- **Validation Corm IoU:** `0.9605193726057429`
 
-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: 6e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 50
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Bg Iou | Damage Iou | Corm Iou | Bg Accuracy | Damage Accuracy | Corm Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:------:|:----------:|:--------:|:-----------:|:---------------:|:-------------:|
-| 0.3602        | 1.0   | 66   | 0.2173          | 0.7170   | 0.7566        | 0.9924 | 0.2919     | 0.8668   | 0.9948      | 0.2934          | 0.9815        |
-| 0.2317        | 2.0   | 132  | 0.1247          | 0.8573   | 0.9279        | 0.9945 | 0.6670     | 0.9103   | 0.9961      | 0.8305          | 0.9571        |
-| 0.1233        | 3.0   | 198  | 0.0776          | 0.8707   | 0.9058        | 0.9955 | 0.6885     | 0.9281   | 0.9964      | 0.7355          | 0.9857        |
-| 0.0877        | 4.0   | 264  | 0.0545          | 0.8893   | 0.9219        | 0.9968 | 0.7299     | 0.9412   | 0.9980      | 0.7863          | 0.9815        |
-| 0.0819        | 5.0   | 330  | 0.0402          | 0.8958   | 0.9359        | 0.9970 | 0.7469     | 0.9433   | 0.9981      | 0.8320          | 0.9777        |
-| 0.0599        | 6.0   | 396  | 0.0331          | 0.9014   | 0.9399        | 0.9971 | 0.7611     | 0.9460   | 0.9983      | 0.8443          | 0.9772        |
-| 0.0476        | 7.0   | 462  | 0.0317          | 0.9021   | 0.9464        | 0.9972 | 0.7625     | 0.9464   | 0.9984      | 0.8668          | 0.9741        |
-| 0.0434        | 8.0   | 528  | 0.0255          | 0.9067   | 0.9388        | 0.9973 | 0.7730     | 0.9499   | 0.9985      | 0.8378          | 0.9800        |
-| 0.0488        | 9.0   | 594  | 0.0264          | 0.9010   | 0.9610        | 0.9973 | 0.7612     | 0.9446   | 0.9984      | 0.9188          | 0.9657        |
-| 0.036         | 10.0  | 660  | 0.0223          | 0.9104   | 0.9431        | 0.9973 | 0.7831     | 0.9507   | 0.9986      | 0.8519          | 0.9788        |
-| 0.0396        | 11.0  | 726  | 0.0213          | 0.9066   | 0.9322        | 0.9974 | 0.7712     | 0.9512   | 0.9984      | 0.8122          | 0.9858        |
-| 0.0279        | 12.0  | 792  | 0.0197          | 0.9135   | 0.9512        | 0.9974 | 0.7905     | 0.9524   | 0.9984      | 0.8764          | 0.9787        |
-| 0.0254        | 13.0  | 858  | 0.0191          | 0.9095   | 0.9603        | 0.9975 | 0.7813     | 0.9496   | 0.9987      | 0.9127          | 0.9694        |
-| 0.0214        | 14.0  | 924  | 0.0191          | 0.9141   | 0.9587        | 0.9975 | 0.7926     | 0.9522   | 0.9986      | 0.9037          | 0.9737        |
-| 0.0237        | 15.0  | 990  | 0.0178          | 0.9164   | 0.9525        | 0.9975 | 0.7977     | 0.9540   | 0.9986      | 0.8799          | 0.9791        |
-| 0.0211        | 16.0  | 1056 | 0.0168          | 0.9126   | 0.9389        | 0.9976 | 0.7862     | 0.9539   | 0.9985      | 0.8332          | 0.9850        |
-| 0.0163        | 17.0  | 1122 | 0.0163          | 0.9173   | 0.9480        | 0.9975 | 0.7993     | 0.9550   | 0.9990      | 0.8665          | 0.9784        |
-| 0.0168        | 18.0  | 1188 | 0.0155          | 0.9203   | 0.9555        | 0.9976 | 0.8070     | 0.9562   | 0.9988      | 0.8895          | 0.9781        |
-| 0.0157        | 19.0  | 1254 | 0.0164          | 0.9145   | 0.9614        | 0.9977 | 0.7931     | 0.9528   | 0.9989      | 0.9146          | 0.9707        |
-| 0.0202        | 20.0  | 1320 | 0.0152          | 0.9203   | 0.9489        | 0.9976 | 0.8063     | 0.9570   | 0.9986      | 0.8642          | 0.9839        |
-| 0.017         | 21.0  | 1386 | 0.0152          | 0.9211   | 0.9510        | 0.9977 | 0.8083     | 0.9572   | 0.9990      | 0.8741          | 0.9800        |
-| 0.0159        | 22.0  | 1452 | 0.0148          | 0.9228   | 0.9546        | 0.9977 | 0.8130     | 0.9577   | 0.9986      | 0.8833          | 0.9818        |
-| 0.018         | 23.0  | 1518 | 0.0141          | 0.9224   | 0.9509        | 0.9977 | 0.8115     | 0.9580   | 0.9989      | 0.8719          | 0.9820        |
-| 0.0176        | 24.0  | 1584 | 0.0139          | 0.9231   | 0.9534        | 0.9977 | 0.8134     | 0.9581   | 0.9988      | 0.8797          | 0.9816        |
-| 0.016         | 25.0  | 1650 | 0.0142          | 0.9188   | 0.9434        | 0.9977 | 0.8013     | 0.9574   | 0.9989      | 0.8472          | 0.9842        |
-| 0.0148        | 26.0  | 1716 | 0.0137          | 0.9241   | 0.9557        | 0.9977 | 0.8160     | 0.9585   | 0.9988      | 0.8869          | 0.9814        |
-| 0.0153        | 27.0  | 1782 | 0.0140          | 0.9241   | 0.9602        | 0.9978 | 0.8162     | 0.9582   | 0.9989      | 0.9044          | 0.9774        |
-| 0.0141        | 28.0  | 1848 | 0.0138          | 0.9236   | 0.9612        | 0.9977 | 0.8154     | 0.9577   | 0.9988      | 0.9073          | 0.9776        |
-| 0.0156        | 29.0  | 1914 | 0.0135          | 0.9247   | 0.9581        | 0.9977 | 0.8179     | 0.9585   | 0.9989      | 0.8966          | 0.9787        |
-| 0.0144        | 30.0  | 1980 | 0.0136          | 0.9243   | 0.9565        | 0.9977 | 0.8170     | 0.9582   | 0.9985      | 0.8880          | 0.9831        |
-| 0.0127        | 31.0  | 2046 | 0.0134          | 0.9246   | 0.9606        | 0.9978 | 0.8173     | 0.9586   | 0.9987      | 0.9034          | 0.9799        |
-| 0.0161        | 32.0  | 2112 | 0.0130          | 0.9254   | 0.9539        | 0.9978 | 0.8188     | 0.9596   | 0.9988      | 0.8803          | 0.9827        |
-| 0.0138        | 33.0  | 2178 | 0.0132          | 0.9252   | 0.9596        | 0.9977 | 0.8191     | 0.9587   | 0.9985      | 0.8983          | 0.9820        |
-| 0.0123        | 34.0  | 2244 | 0.0133          | 0.9250   | 0.9588        | 0.9978 | 0.8184     | 0.9588   | 0.9988      | 0.8980          | 0.9796        |
-| 0.0139        | 35.0  | 2310 | 0.0129          | 0.9256   | 0.9566        | 0.9978 | 0.8193     | 0.9596   | 0.9989      | 0.8903          | 0.9806        |
-| 0.0138        | 36.0  | 2376 | 0.0130          | 0.9260   | 0.9618        | 0.9978 | 0.8209     | 0.9593   | 0.9989      | 0.9081          | 0.9784        |
-| 0.0143        | 37.0  | 2442 | 0.0128          | 0.9244   | 0.9509        | 0.9978 | 0.8159     | 0.9596   | 0.9988      | 0.8698          | 0.9841        |
-| 0.0122        | 38.0  | 2508 | 0.0128          | 0.9246   | 0.9515        | 0.9978 | 0.8164     | 0.9597   | 0.9988      | 0.8711          | 0.9846        |
-| 0.014         | 39.0  | 2574 | 0.0130          | 0.9244   | 0.9634        | 0.9978 | 0.8170     | 0.9585   | 0.9989      | 0.9145          | 0.9769        |
-| 0.0126        | 40.0  | 2640 | 0.0125          | 0.9269   | 0.9611        | 0.9978 | 0.8233     | 0.9598   | 0.9990      | 0.9057          | 0.9786        |
-| 0.0142        | 41.0  | 2706 | 0.0126          | 0.9270   | 0.9613        | 0.9978 | 0.8232     | 0.9598   | 0.9988      | 0.9048          | 0.9802        |
-| 0.012         | 42.0  | 2772 | 0.0126          | 0.9266   | 0.9653        | 0.9978 | 0.8226     | 0.9593   | 0.9988      | 0.9191          | 0.9781        |
-| 0.0128        | 43.0  | 2838 | 0.0125          | 0.9268   | 0.9558        | 0.9978 | 0.8223     | 0.9602   | 0.9988      | 0.8857          | 0.9829        |
-| 0.0145        | 44.0  | 2904 | 0.0126          | 0.9268   | 0.9638        | 0.9978 | 0.8231     | 0.9595   | 0.9989      | 0.9146          | 0.9778        |
-| 0.0118        | 45.0  | 2970 | 0.0127          | 0.9278   | 0.9613        | 0.9978 | 0.8251     | 0.9603   | 0.9989      | 0.9050          | 0.9799        |
-| 0.0118        | 46.0  | 3036 | 0.0122          | 0.9276   | 0.9582        | 0.9978 | 0.8246     | 0.9604   | 0.9990      | 0.8954          | 0.9803        |
-| 0.0138        | 47.0  | 3102 | 0.0124          | 0.9273   | 0.9564        | 0.9978 | 0.8235     | 0.9605   | 0.9989      | 0.8883          | 0.9818        |
-| 0.0129        | 48.0  | 3168 | 0.0123          | 0.9279   | 0.9608        | 0.9979 | 0.8253     | 0.9604   | 0.9989      | 0.9034          | 0.9800        |
-| 0.0107        | 49.0  | 3234 | 0.0124          | 0.9277   | 0.9600        | 0.9978 | 0.8249     | 0.9604   | 0.9988      | 0.9001          | 0.9811        |
-| 0.0131        | 50.0  | 3300 | 0.0123          | 0.9276   | 0.9628        | 0.9978 | 0.8250     | 0.9600   | 0.9991      | 0.9120          | 0.9773        |
-
-
-### Framework versions
-
-- Transformers 4.44.2
-- Pytorch 2.4.0+cu118
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+## Metric Curves
+Below are the training performance plots generated for this run:
+![Performance Curves](performance_curves.png)

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+  ],
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+  "transformers_version": "4.44.2"
+}

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