NICOPOI-9
/

segformer-b0-finetuned-morphpadver1-hgo-coord-v9_mix_resample_40epochs

@@ -1,199 +1,99 @@
----
-library_name: transformers
-tags: []
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+---
+library_name: transformers
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-morphpadver1-hgo-coord-v9_mix_resample_40epochs
+  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-b0-finetuned-morphpadver1-hgo-coord-v9_mix_resample_40epochs
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8517
+- Mean Iou: 0.5269
+- Mean Accuracy: 0.6857
+- Overall Accuracy: 0.6922
+- Accuracy 0-0: 0.5774
+- Accuracy 0-90: 0.7340
+- Accuracy 90-0: 0.7692
+- Accuracy 90-90: 0.6625
+- Iou 0-0: 0.4919
+- Iou 0-90: 0.5434
+- Iou 90-0: 0.5346
+- Iou 90-90: 0.5380
+## 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 |
+|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
+| 1.4178        | 1.3638  | 4000   | 1.4017          | 0.0915   | 0.2543        | 0.2763           | 0.0012       | 0.1224        | 0.8886        | 0.0052         | 0.0012  | 0.0995   | 0.2601   | 0.0052    |
+| 1.1726        | 2.7276  | 8000   | 1.3327          | 0.2022   | 0.3465        | 0.3633           | 0.1781       | 0.4945        | 0.5489        | 0.1646         | 0.1323  | 0.2709   | 0.2783   | 0.1273    |
+| 1.3111        | 4.0914  | 12000  | 1.3034          | 0.2235   | 0.3684        | 0.3811           | 0.2368       | 0.5790        | 0.4089        | 0.2489         | 0.1621  | 0.3039   | 0.2584   | 0.1694    |
+| 1.033         | 5.4552  | 16000  | 1.2837          | 0.2340   | 0.3853        | 0.4006           | 0.1897       | 0.5159        | 0.5735        | 0.2619         | 0.1526  | 0.2965   | 0.3101   | 0.1769    |
+| 1.3103        | 6.8190  | 20000  | 1.2502          | 0.2593   | 0.4171        | 0.4339           | 0.2446       | 0.6314        | 0.5467        | 0.2456         | 0.1839  | 0.3469   | 0.3212   | 0.1851    |
+| 0.6831        | 8.1827  | 24000  | 1.2405          | 0.2655   | 0.4238        | 0.4336           | 0.2449       | 0.3989        | 0.6621        | 0.3893         | 0.1849  | 0.2957   | 0.3333   | 0.2482    |
+| 1.1638        | 9.5465  | 28000  | 1.1866          | 0.2955   | 0.4566        | 0.4696           | 0.3300       | 0.6108        | 0.5695        | 0.3160         | 0.2396  | 0.3484   | 0.3479   | 0.2463    |
+| 1.2145        | 10.9103 | 32000  | 1.1129          | 0.3356   | 0.5008        | 0.5092           | 0.4052       | 0.5818        | 0.5926        | 0.4236         | 0.2913  | 0.3764   | 0.3705   | 0.3042    |
+| 0.767         | 12.2741 | 36000  | 1.1059          | 0.3423   | 0.5078        | 0.5144           | 0.4463       | 0.5576        | 0.5978        | 0.4295         | 0.3098  | 0.3613   | 0.3732   | 0.3250    |
+| 1.0089        | 13.6379 | 40000  | 1.0832          | 0.3500   | 0.5157        | 0.5252           | 0.4129       | 0.6431        | 0.5790        | 0.4280         | 0.3054  | 0.3812   | 0.3870   | 0.3263    |
+| 1.0757        | 15.0017 | 44000  | 1.0207          | 0.3866   | 0.5553        | 0.5626           | 0.4802       | 0.6133        | 0.6502        | 0.4776         | 0.3529  | 0.4112   | 0.4246   | 0.3577    |
+| 0.8842        | 16.3655 | 48000  | 1.0716          | 0.3737   | 0.5417        | 0.5529           | 0.4372       | 0.6738        | 0.6390        | 0.4169         | 0.3371  | 0.4152   | 0.4191   | 0.3234    |
+| 0.8464        | 17.7293 | 52000  | 1.0188          | 0.4101   | 0.5795        | 0.5884           | 0.4262       | 0.6296        | 0.7147        | 0.5474         | 0.3467  | 0.4325   | 0.4543   | 0.4069    |
+| 0.8371        | 19.0931 | 56000  | 0.9905          | 0.4260   | 0.5942        | 0.6027           | 0.4614       | 0.6846        | 0.6765        | 0.5542         | 0.3766  | 0.4455   | 0.4655   | 0.4166    |
+| 0.7882        | 20.4569 | 60000  | 0.9542          | 0.4454   | 0.6126        | 0.6216           | 0.4838       | 0.6737        | 0.7397        | 0.5530         | 0.3925  | 0.4665   | 0.4815   | 0.4411    |
+| 2.4763        | 21.8207 | 64000  | 0.9188          | 0.4671   | 0.6330        | 0.6402           | 0.5338       | 0.6708        | 0.7484        | 0.5788         | 0.4359  | 0.4820   | 0.4932   | 0.4572    |
+| 0.3528        | 23.1845 | 68000  | 0.8817          | 0.4725   | 0.6381        | 0.6450           | 0.5270       | 0.6813        | 0.7379        | 0.6063         | 0.4314  | 0.4937   | 0.4905   | 0.4745    |
+| 0.8088        | 24.5482 | 72000  | 0.9115          | 0.4800   | 0.6458        | 0.6500           | 0.5807       | 0.6461        | 0.7349        | 0.6217         | 0.4507  | 0.4844   | 0.4938   | 0.4912    |
+| 0.8153        | 25.9120 | 76000  | 0.9558          | 0.4531   | 0.6215        | 0.6342           | 0.3715       | 0.7059        | 0.7951        | 0.6135         | 0.3382  | 0.5023   | 0.4889   | 0.4829    |
+| 0.9085        | 27.2758 | 80000  | 0.9089          | 0.4777   | 0.6415        | 0.6542           | 0.4936       | 0.7556        | 0.7928        | 0.5238         | 0.4312  | 0.5149   | 0.5148   | 0.4500    |
+| 0.3666        | 28.6396 | 84000  | 1.0426          | 0.4467   | 0.6141        | 0.6270           | 0.3862       | 0.6873        | 0.8064        | 0.5767         | 0.3460  | 0.5000   | 0.4754   | 0.4654    |
+| 0.6065        | 30.0034 | 88000  | 0.9086          | 0.4850   | 0.6497        | 0.6557           | 0.5433       | 0.6885        | 0.7346        | 0.6323         | 0.4404  | 0.5002   | 0.5009   | 0.4985    |
+| 0.1385        | 31.3672 | 92000  | 0.9247          | 0.4688   | 0.6343        | 0.6469           | 0.4228       | 0.7420        | 0.7832        | 0.5892         | 0.3792  | 0.5132   | 0.4999   | 0.4829    |
+| 0.4116        | 32.7310 | 96000  | 0.8724          | 0.5014   | 0.6628        | 0.6707           | 0.5288       | 0.6729        | 0.8213        | 0.6281         | 0.4585  | 0.5268   | 0.5094   | 0.5112    |
+| 0.4991        | 34.0948 | 100000 | 0.8752          | 0.5078   | 0.6693        | 0.6766           | 0.5435       | 0.7342        | 0.7515        | 0.6480         | 0.4584  | 0.5274   | 0.5232   | 0.5225    |
+| 0.5235        | 35.4586 | 104000 | 0.8312          | 0.5135   | 0.6736        | 0.6814           | 0.6179       | 0.7514        | 0.7598        | 0.5651         | 0.5060  | 0.5362   | 0.5256   | 0.4861    |
+| 0.6378        | 36.8224 | 108000 | 0.8729          | 0.5102   | 0.6705        | 0.6784           | 0.5636       | 0.7161        | 0.7926        | 0.6097         | 0.4781  | 0.5335   | 0.5216   | 0.5076    |
+| 0.6895        | 38.1862 | 112000 | 0.9258          | 0.4833   | 0.6466        | 0.6600           | 0.4375       | 0.7335        | 0.8392        | 0.5761         | 0.3990  | 0.5343   | 0.5097   | 0.4903    |
+| 0.5259        | 39.5499 | 116000 | 0.8517          | 0.5269   | 0.6857        | 0.6922           | 0.5774       | 0.7340        | 0.7692        | 0.6625         | 0.4919  | 0.5434   | 0.5346   | 0.5380    |
+### 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/mit-b0",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    32,
+    64,
+    160,
+    256
+  ],
+  "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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