Hasano20
/

SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium

@@ -1,199 +1,99 @@
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-library_name: transformers
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 ---
+license: other
+base_model: nvidia/mit-b5
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium
+  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_mit-b5_Clean-Set3-Grayscale_Augmented_Medium
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean-Set3-Grayscale dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0134
+- Mean Iou: 0.9793
+- Mean Accuracy: 0.9903
+- Overall Accuracy: 0.9947
+- Accuracy Background: 0.9971
+- Accuracy Melt: 0.9785
+- Accuracy Substrate: 0.9952
+- Iou Background: 0.9935
+- Iou Melt: 0.9524
+- Iou Substrate: 0.9920
+## 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: 0.0002
+- 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: cosine
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 25
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1305        | 0.3968  | 50   | 0.1020          | 0.8694   | 0.9199        | 0.9644           | 0.9855              | 0.8016        | 0.9726             | 0.9651         | 0.6989   | 0.9443        |
+| 0.0906        | 0.7937  | 100  | 0.0668          | 0.8972   | 0.9187        | 0.9757           | 0.9891              | 0.7703        | 0.9968             | 0.9818         | 0.7488   | 0.9609        |
+| 0.0409        | 1.1905  | 150  | 0.0606          | 0.9231   | 0.9414        | 0.9814           | 0.9879              | 0.8379        | 0.9984             | 0.9840         | 0.8152   | 0.9702        |
+| 0.0678        | 1.5873  | 200  | 0.0344          | 0.9524   | 0.9762        | 0.9879           | 0.9883              | 0.9463        | 0.9941             | 0.9848         | 0.8890   | 0.9834        |
+| 0.0312        | 1.9841  | 250  | 0.0340          | 0.9489   | 0.9756        | 0.9874           | 0.9935              | 0.9442        | 0.9892             | 0.9869         | 0.8779   | 0.9818        |
+| 0.0334        | 2.3810  | 300  | 0.0277          | 0.9576   | 0.9826        | 0.9895           | 0.9956              | 0.9637        | 0.9885             | 0.9908         | 0.8987   | 0.9833        |
+| 0.0286        | 2.7778  | 350  | 0.0264          | 0.9581   | 0.9776        | 0.9898           | 0.9964              | 0.9452        | 0.9912             | 0.9896         | 0.9002   | 0.9846        |
+| 0.0214        | 3.1746  | 400  | 0.0230          | 0.9661   | 0.9824        | 0.9915           | 0.9926              | 0.9587        | 0.9958             | 0.9903         | 0.9206   | 0.9875        |
+| 0.0208        | 3.5714  | 450  | 0.0203          | 0.9692   | 0.9876        | 0.9922           | 0.9968              | 0.9751        | 0.9910             | 0.9916         | 0.9283   | 0.9878        |
+| 0.0146        | 3.9683  | 500  | 0.0231          | 0.9667   | 0.9852        | 0.9915           | 0.9961              | 0.9680        | 0.9913             | 0.9904         | 0.9229   | 0.9870        |
+| 0.0197        | 4.3651  | 550  | 0.0208          | 0.9662   | 0.9883        | 0.9916           | 0.9950              | 0.9790        | 0.9908             | 0.9914         | 0.9200   | 0.9873        |
+| 0.0198        | 4.7619  | 600  | 0.0184          | 0.9722   | 0.9836        | 0.9930           | 0.9969              | 0.9587        | 0.9951             | 0.9916         | 0.9355   | 0.9896        |
+| 0.019         | 5.1587  | 650  | 0.0211          | 0.9693   | 0.9889        | 0.9919           | 0.9970              | 0.9801        | 0.9896             | 0.9907         | 0.9298   | 0.9872        |
+| 0.0115        | 5.5556  | 700  | 0.0193          | 0.9706   | 0.9833        | 0.9928           | 0.9963              | 0.9584        | 0.9953             | 0.9926         | 0.9304   | 0.9888        |
+| 0.0135        | 5.9524  | 750  | 0.0166          | 0.9740   | 0.9867        | 0.9933           | 0.9965              | 0.9692        | 0.9945             | 0.9919         | 0.9401   | 0.9899        |
+| 0.0127        | 6.3492  | 800  | 0.0182          | 0.9736   | 0.9866        | 0.9932           | 0.9969              | 0.9689        | 0.9939             | 0.9918         | 0.9395   | 0.9895        |
+| 0.0129        | 6.7460  | 850  | 0.0194          | 0.9723   | 0.9853        | 0.9930           | 0.9958              | 0.9651        | 0.9951             | 0.9920         | 0.9354   | 0.9894        |
+| 0.0124        | 7.1429  | 900  | 0.0145          | 0.9771   | 0.9900        | 0.9941           | 0.9972              | 0.9789        | 0.9940             | 0.9928         | 0.9472   | 0.9911        |
+| 0.011         | 7.5397  | 950  | 0.0149          | 0.9774   | 0.9876        | 0.9941           | 0.9972              | 0.9704        | 0.9953             | 0.9923         | 0.9485   | 0.9914        |
+| 0.0176        | 7.9365  | 1000 | 0.0212          | 0.9681   | 0.9890        | 0.9919           | 0.9972              | 0.9802        | 0.9895             | 0.9923         | 0.9251   | 0.9869        |
+| 0.0205        | 8.3333  | 1050 | 0.0171          | 0.9724   | 0.9895        | 0.9930           | 0.9971              | 0.9797        | 0.9918             | 0.9924         | 0.9356   | 0.9893        |
+| 0.0103        | 8.7302  | 1100 | 0.0141          | 0.9780   | 0.9891        | 0.9943           | 0.9968              | 0.9754        | 0.9953             | 0.9928         | 0.9497   | 0.9915        |
+| 0.0093        | 9.1270  | 1150 | 0.0148          | 0.9769   | 0.9881        | 0.9941           | 0.9965              | 0.9723        | 0.9956             | 0.9930         | 0.9466   | 0.9911        |
+| 0.0113        | 9.5238  | 1200 | 0.0136          | 0.9788   | 0.9881        | 0.9945           | 0.9977              | 0.9711        | 0.9955             | 0.9929         | 0.9517   | 0.9918        |
+| 0.0132        | 9.9206  | 1250 | 0.0144          | 0.9783   | 0.9882        | 0.9944           | 0.9971              | 0.9720        | 0.9957             | 0.9930         | 0.9503   | 0.9915        |
+| 0.0104        | 10.3175 | 1300 | 0.0135          | 0.9788   | 0.9882        | 0.9945           | 0.9976              | 0.9714        | 0.9957             | 0.9932         | 0.9515   | 0.9918        |
+| 0.0153        | 10.7143 | 1350 | 0.0129          | 0.9796   | 0.9889        | 0.9947           | 0.9970              | 0.9734        | 0.9962             | 0.9932         | 0.9534   | 0.9922        |
+| 0.0091        | 11.1111 | 1400 | 0.0142          | 0.9783   | 0.9900        | 0.9944           | 0.9968              | 0.9784        | 0.9950             | 0.9931         | 0.9500   | 0.9917        |
+| 0.0098        | 11.5079 | 1450 | 0.0139          | 0.9789   | 0.9889        | 0.9946           | 0.9967              | 0.9740        | 0.9962             | 0.9933         | 0.9516   | 0.9920        |
+| 0.0094        | 11.9048 | 1500 | 0.0136          | 0.9795   | 0.9887        | 0.9947           | 0.9977              | 0.9730        | 0.9956             | 0.9931         | 0.9533   | 0.9920        |
+| 0.0088        | 12.3016 | 1550 | 0.0134          | 0.9793   | 0.9903        | 0.9947           | 0.9971              | 0.9785        | 0.9952             | 0.9935         | 0.9524   | 0.9920        |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json ADDED Viewed

	@@ -0,0 +1,80 @@

+{
+  "_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": "background",
+    "1": "melt",
+    "2": "substrate"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "background": 0,
+    "melt": 1,
+    "substrate": 2
+  },
+  "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": "4.41.2"
+}

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