NICOPOI-9
/

segformer-b5-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-b5
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b5-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-b5-finetuned-morphpadver1-hgo-coord-v9_mix_resample_40epochs
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6422
+- Mean Iou: 0.7347
+- Mean Accuracy: 0.8436
+- Overall Accuracy: 0.8478
+- Accuracy 0-0: 0.7840
+- Accuracy 0-90: 0.8932
+- Accuracy 90-0: 0.8779
+- Accuracy 90-90: 0.8194
+- Iou 0-0: 0.7200
+- Iou 0-90: 0.7429
+- Iou 90-0: 0.7413
+- Iou 90-90: 0.7348
+## 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 |
+|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
+| 1.3684        | 1.3638  | 4000   | 1.3355          | 0.1756   | 0.3310        | 0.3524           | 0.1001       | 0.3549        | 0.7755        | 0.0937         | 0.0866  | 0.2432   | 0.2910   | 0.0815    |
+| 0.6699        | 2.7276  | 8000   | 1.1446          | 0.3199   | 0.4833        | 0.4927           | 0.3842       | 0.5642        | 0.5994        | 0.3853         | 0.2808  | 0.3636   | 0.3545   | 0.2808    |
+| 0.7075        | 4.0914  | 12000  | 1.0016          | 0.4314   | 0.6036        | 0.6040           | 0.6056       | 0.5476        | 0.6773        | 0.5840         | 0.4196  | 0.4287   | 0.4540   | 0.4233    |
+| 0.4978        | 5.4552  | 16000  | 0.8600          | 0.5036   | 0.6641        | 0.6709           | 0.5657       | 0.7738        | 0.6865        | 0.6305         | 0.4895  | 0.5093   | 0.5085   | 0.5071    |
+| 0.6194        | 6.8190  | 20000  | 0.8307          | 0.5419   | 0.7019        | 0.7030           | 0.6992       | 0.7035        | 0.7266        | 0.6782         | 0.5500  | 0.5426   | 0.5441   | 0.5311    |
+| 0.2622        | 8.1827  | 24000  | 0.7177          | 0.5987   | 0.7469        | 0.7491           | 0.7321       | 0.7660        | 0.7749        | 0.7148         | 0.5962  | 0.5985   | 0.6019   | 0.5984    |
+| 0.9683        | 9.5465  | 28000  | 0.7541          | 0.5951   | 0.7409        | 0.7474           | 0.6970       | 0.7901        | 0.8298        | 0.6467         | 0.5996  | 0.6063   | 0.6010   | 0.5736    |
+| 0.2542        | 10.9103 | 32000  | 0.7039          | 0.6381   | 0.7768        | 0.7791           | 0.7554       | 0.7818        | 0.8233        | 0.7465         | 0.6333  | 0.6498   | 0.6295   | 0.6400    |
+| 0.1691        | 12.2741 | 36000  | 0.6232          | 0.6632   | 0.7959        | 0.7978           | 0.7673       | 0.8280        | 0.8005        | 0.7877         | 0.6636  | 0.6778   | 0.6536   | 0.6580    |
+| 0.1883        | 13.6379 | 40000  | 0.6711          | 0.6649   | 0.7948        | 0.7996           | 0.7315       | 0.8565        | 0.8291        | 0.7622         | 0.6514  | 0.6774   | 0.6675   | 0.6632    |
+| 0.164         | 15.0017 | 44000  | 0.6627          | 0.6688   | 0.7980        | 0.8022           | 0.7670       | 0.8227        | 0.8637        | 0.7386         | 0.6740  | 0.6846   | 0.6687   | 0.6479    |
+| 0.2406        | 16.3655 | 48000  | 0.6364          | 0.6930   | 0.8159        | 0.8194           | 0.7843       | 0.8565        | 0.8466        | 0.7763         | 0.6894  | 0.7005   | 0.7017   | 0.6805    |
+| 0.109         | 17.7293 | 52000  | 0.6087          | 0.6872   | 0.8119        | 0.8153           | 0.7622       | 0.8473        | 0.8443        | 0.7940         | 0.6733  | 0.7055   | 0.6834   | 0.6868    |
+| 0.1262        | 19.0931 | 56000  | 0.6101          | 0.6999   | 0.8202        | 0.8240           | 0.7795       | 0.8572        | 0.8619        | 0.7823         | 0.6912  | 0.7071   | 0.7041   | 0.6972    |
+| 0.1633        | 20.4569 | 60000  | 0.6434          | 0.7056   | 0.8239        | 0.8280           | 0.7832       | 0.8548        | 0.8795        | 0.7781         | 0.7006  | 0.7194   | 0.7056   | 0.6969    |
+| 8.0069        | 21.8207 | 64000  | 0.5640          | 0.7111   | 0.8286        | 0.8319           | 0.8192       | 0.8718        | 0.8567        | 0.7666         | 0.7149  | 0.7267   | 0.7119   | 0.6909    |
+| 0.0335        | 23.1845 | 68000  | 0.5820          | 0.7215   | 0.8348        | 0.8388           | 0.7894       | 0.8701        | 0.8828        | 0.7967         | 0.7085  | 0.7253   | 0.7293   | 0.7230    |
+| 0.3274        | 24.5482 | 72000  | 0.6041          | 0.7210   | 0.8346        | 0.8386           | 0.7929       | 0.8767        | 0.8754        | 0.7933         | 0.7157  | 0.7344   | 0.7223   | 0.7117    |
+| 0.137         | 25.9120 | 76000  | 0.6174          | 0.7000   | 0.8197        | 0.8246           | 0.7584       | 0.8768        | 0.8609        | 0.7829         | 0.6897  | 0.7138   | 0.7079   | 0.6886    |
+| 0.0973        | 27.2758 | 80000  | 0.6329          | 0.7039   | 0.8208        | 0.8276           | 0.7483       | 0.9023        | 0.8783        | 0.7542         | 0.6896  | 0.7184   | 0.7139   | 0.6936    |
+| 0.0938        | 28.6396 | 84000  | 0.5952          | 0.7273   | 0.8412        | 0.8421           | 0.8351       | 0.8486        | 0.8535        | 0.8276         | 0.7316  | 0.7346   | 0.7211   | 0.7218    |
+| 0.0558        | 30.0034 | 88000  | 0.6204          | 0.7017   | 0.8193        | 0.8260           | 0.7578       | 0.8958        | 0.8822        | 0.7412         | 0.6987  | 0.7170   | 0.7090   | 0.6822    |
+| 0.0048        | 31.3672 | 92000  | 0.6403          | 0.7057   | 0.8219        | 0.8289           | 0.7465       | 0.8980        | 0.8915        | 0.7514         | 0.6933  | 0.7244   | 0.7130   | 0.6920    |
+| 0.0134        | 32.7310 | 96000  | 0.6758          | 0.7192   | 0.8333        | 0.8375           | 0.8073       | 0.8800        | 0.8754        | 0.7704         | 0.7150  | 0.7309   | 0.7256   | 0.7052    |
+| 0.0326        | 34.0948 | 100000 | 0.6023          | 0.7256   | 0.8362        | 0.8419           | 0.7617       | 0.9030        | 0.8856        | 0.7944         | 0.7094  | 0.7337   | 0.7341   | 0.7254    |
+| 0.0379        | 35.4586 | 104000 | 0.6208          | 0.7342   | 0.8436        | 0.8472           | 0.7932       | 0.8949        | 0.8641        | 0.8220         | 0.7245  | 0.7368   | 0.7419   | 0.7334    |
+| 0.0676        | 36.8224 | 108000 | 0.6448          | 0.7298   | 0.8401        | 0.8446           | 0.7877       | 0.8896        | 0.8831        | 0.8001         | 0.7180  | 0.7440   | 0.7336   | 0.7236    |
+| 0.0604        | 38.1862 | 112000 | 0.6608          | 0.7334   | 0.8426        | 0.8468           | 0.7867       | 0.8845        | 0.8869        | 0.8124         | 0.7214  | 0.7441   | 0.7364   | 0.7315    |
+| 0.018         | 39.5499 | 116000 | 0.6422          | 0.7347   | 0.8436        | 0.8478           | 0.7840       | 0.8932        | 0.8779        | 0.8194         | 0.7200  | 0.7429   | 0.7413   | 0.7348    |
+### 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-b5",
+  "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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