NICOPOI-9
/

segformer-b5-finetuned-ade20k-morphpadver1-hgo-coord_40epochs_distortion_global_norm

@@ -1,199 +1,99 @@
----
-library_name: transformers
-tags: []
----
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+---
+library_name: transformers
+license: other
+base_model: nvidia/segformer-b5-finetuned-ade-640-640
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b5-finetuned-ade20k-morphpadver1-hgo-coord_40epochs_distortion_global_norm
+  results: []
+---
+<!-- 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-b5-finetuned-ade20k-morphpadver1-hgo-coord_40epochs_distortion_global_norm
+This model is a fine-tuned version of [nvidia/segformer-b5-finetuned-ade-640-640](https://huggingface.co/nvidia/segformer-b5-finetuned-ade-640-640) on the NICOPOI-9/Morphpad_HGO_1600_coord_global_norm dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1027
+- Mean Iou: 0.9780
+- Mean Accuracy: 0.9889
+- Overall Accuracy: 0.9886
+- Accuracy 0-0: 0.9903
+- Accuracy 0-90: 0.9850
+- Accuracy 90-0: 0.9874
+- Accuracy 90-90: 0.9929
+- Iou 0-0: 0.9819
+- Iou 0-90: 0.9740
+- Iou 90-0: 0.9729
+- Iou 90-90: 0.9831
+## 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: 6e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 40
+### Training results
+| Training Loss | Epoch   | Step   | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 0-0 | Accuracy 0-90 | Accuracy 90-0 | Accuracy 90-90 | Iou 0-0 | Iou 0-90 | Iou 90-0 | Iou 90-90 |
+|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
+| 1.2959        | 1.3680  | 4000   | 1.2209          | 0.2639   | 0.4166        | 0.4247           | 0.2827       | 0.4642        | 0.5298        | 0.3899         | 0.2232  | 0.2838   | 0.2934   | 0.2550    |
+| 0.8526        | 2.7360  | 8000   | 0.9446          | 0.4145   | 0.5789        | 0.5841           | 0.5010       | 0.6754        | 0.5810        | 0.5582         | 0.4193  | 0.4020   | 0.4076   | 0.4290    |
+| 0.6552        | 4.1040  | 12000  | 0.7331          | 0.5293   | 0.6854        | 0.6880           | 0.6425       | 0.6769        | 0.7503        | 0.6717         | 0.5626  | 0.4909   | 0.5151   | 0.5485    |
+| 0.4479        | 5.4720  | 16000  | 0.5873          | 0.6216   | 0.7621        | 0.7634           | 0.7338       | 0.8038        | 0.7429        | 0.7677         | 0.6463  | 0.5867   | 0.6042   | 0.6494    |
+| 0.3862        | 6.8399  | 20000  | 0.4229          | 0.7262   | 0.8411        | 0.8396           | 0.8751       | 0.8489        | 0.8048        | 0.8354         | 0.7527  | 0.6991   | 0.7079   | 0.7449    |
+| 1.3206        | 8.2079  | 24000  | 0.2964          | 0.8276   | 0.9031        | 0.9040           | 0.8928       | 0.9069        | 0.9194        | 0.8932         | 0.8544  | 0.8091   | 0.8044   | 0.8427    |
+| 1.2794        | 9.5759  | 28000  | 0.2393          | 0.8588   | 0.9236        | 0.9229           | 0.9416       | 0.8933        | 0.9435        | 0.9160         | 0.8776  | 0.8406   | 0.8410   | 0.8760    |
+| 0.1104        | 10.9439 | 32000  | 0.1972          | 0.8876   | 0.9409        | 0.9396           | 0.9546       | 0.9336        | 0.9196        | 0.9559         | 0.9028  | 0.8736   | 0.8700   | 0.9040    |
+| 0.5852        | 12.3119 | 36000  | 0.1674          | 0.9102   | 0.9536        | 0.9523           | 0.9572       | 0.9438        | 0.9339        | 0.9794         | 0.9167  | 0.9024   | 0.8896   | 0.9320    |
+| 0.0711        | 13.6799 | 40000  | 0.1398          | 0.9289   | 0.9629        | 0.9625           | 0.9648       | 0.9542        | 0.9641        | 0.9684         | 0.9374  | 0.9178   | 0.9169   | 0.9435    |
+| 0.5558        | 15.0479 | 44000  | 0.1391          | 0.9337   | 0.9656        | 0.9651           | 0.9677       | 0.9580        | 0.9638        | 0.9728         | 0.9424  | 0.9235   | 0.9227   | 0.9461    |
+| 1.2811        | 16.4159 | 48000  | 0.1252          | 0.9426   | 0.9706        | 0.9700           | 0.9776       | 0.9681        | 0.9591        | 0.9777         | 0.9521  | 0.9341   | 0.9326   | 0.9516    |
+| 1.1932        | 17.7839 | 52000  | 0.1162          | 0.9486   | 0.9740        | 0.9733           | 0.9811       | 0.9641        | 0.9690        | 0.9818         | 0.9553  | 0.9404   | 0.9428   | 0.9558    |
+| 1.2719        | 19.1518 | 56000  | 0.1141          | 0.9533   | 0.9759        | 0.9757           | 0.9744       | 0.9717        | 0.9761        | 0.9814         | 0.9581  | 0.9487   | 0.9428   | 0.9638    |
+| 0.0857        | 20.5198 | 60000  | 0.1049          | 0.9590   | 0.9790        | 0.9787           | 0.9783       | 0.9746        | 0.9761        | 0.9870         | 0.9655  | 0.9531   | 0.9508   | 0.9665    |
+| 1.1869        | 21.8878 | 64000  | 0.1069          | 0.9558   | 0.9774        | 0.9770           | 0.9781       | 0.9625        | 0.9826        | 0.9865         | 0.9641  | 0.9494   | 0.9448   | 0.9649    |
+| 0.0294        | 23.2558 | 68000  | 0.1028          | 0.9641   | 0.9819        | 0.9814           | 0.9840       | 0.9772        | 0.9760        | 0.9903         | 0.9680  | 0.9586   | 0.9564   | 0.9733    |
+| 0.5373        | 24.6238 | 72000  | 0.1089          | 0.9639   | 0.9815        | 0.9813           | 0.9813       | 0.9771        | 0.9807        | 0.9869         | 0.9696  | 0.9584   | 0.9564   | 0.9711    |
+| 0.7069        | 25.9918 | 76000  | 0.1026          | 0.9683   | 0.9840        | 0.9836           | 0.9859       | 0.9764        | 0.9842        | 0.9893         | 0.9734  | 0.9630   | 0.9620   | 0.9750    |
+| 0.0382        | 27.3598 | 80000  | 0.1075          | 0.9647   | 0.9818        | 0.9819           | 0.9748       | 0.9835        | 0.9800        | 0.9890         | 0.9627  | 0.9630   | 0.9591   | 0.9739    |
+| 0.0143        | 28.7278 | 84000  | 0.1082          | 0.9719   | 0.9858        | 0.9855           | 0.9896       | 0.9804        | 0.9864        | 0.9866         | 0.9778  | 0.9679   | 0.9654   | 0.9766    |
+| 1.2277        | 30.0958 | 88000  | 0.0961          | 0.9730   | 0.9864        | 0.9861           | 0.9889       | 0.9818        | 0.9829        | 0.9922         | 0.9773  | 0.9685   | 0.9671   | 0.9791    |
+| 1.1838        | 31.4637 | 92000  | 0.0947          | 0.9739   | 0.9868        | 0.9865           | 0.9899       | 0.9834        | 0.9846        | 0.9893         | 0.9785  | 0.9693   | 0.9679   | 0.9801    |
+| 1.2587        | 32.8317 | 96000  | 0.0925          | 0.9745   | 0.9871        | 0.9869           | 0.9902       | 0.9827        | 0.9864        | 0.9893         | 0.9792  | 0.9700   | 0.9689   | 0.9801    |
+| 0.0103        | 34.1997 | 100000 | 0.0988          | 0.9748   | 0.9871        | 0.9870           | 0.9865       | 0.9844        | 0.9869        | 0.9907         | 0.9782  | 0.9713   | 0.9693   | 0.9803    |
+| 1.2995        | 35.5677 | 104000 | 0.0980          | 0.9763   | 0.9880        | 0.9878           | 0.9887       | 0.9838        | 0.9874        | 0.9921         | 0.9805  | 0.9727   | 0.9708   | 0.9813    |
+| 0.0678        | 36.9357 | 108000 | 0.1043          | 0.9778   | 0.9888        | 0.9886           | 0.9899       | 0.9857        | 0.9873        | 0.9922         | 0.9812  | 0.9743   | 0.9722   | 0.9835    |
+| 0.0094        | 38.3037 | 112000 | 0.1031          | 0.9775   | 0.9886        | 0.9884           | 0.9896       | 0.9848        | 0.9884        | 0.9916         | 0.9816  | 0.9742   | 0.9717   | 0.9826    |
+| 0.1925        | 39.6717 | 116000 | 0.1027          | 0.9780   | 0.9889        | 0.9886           | 0.9903       | 0.9850        | 0.9874        | 0.9929         | 0.9819  | 0.9740   | 0.9729   | 0.9831    |
+### Framework versions
+- Transformers 4.48.3
+- Pytorch 2.1.0
+- Datasets 3.2.0
+- Tokenizers 0.21.0

config.json ADDED Viewed

	@@ -0,0 +1,82 @@

+{
+  "_name_or_path": "nvidia/segformer-b5-finetuned-ade-640-640",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    6,
+    40,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "0-0",
+    "1": "0-90",
+    "2": "90-0",
+    "3": "90-90"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "0-0": 0,
+    "0-90": 1,
+    "90-0": 2,
+    "90-90": 3
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3"
+}

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