Hasano20
/

segformer_mixed-set2-788images_mit-b5_RGB_15Epochs

@@ -1,199 +1,90 @@
 ---
-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_Mixed_Set2_788images_mit-b5_RGB
+  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_Mixed_Set2_788images_mit-b5_RGB
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Mixed_Set2_788images dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0150
+- Mean Iou: 0.9788
+- Mean Accuracy: 0.9887
+- Overall Accuracy: 0.9948
+- Accuracy Background: 0.9958
+- Accuracy Melt: 0.9735
+- Accuracy Substrate: 0.9969
+- Iou Background: 0.9926
+- Iou Melt: 0.9509
+- Iou Substrate: 0.9927
+## 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: 0.0001
+- 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: 50
+### 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1619        | 0.7042  | 50   | 0.1799          | 0.7782   | 0.8306        | 0.9444           | 0.9902              | 0.5371        | 0.9645             | 0.9436         | 0.4720   | 0.9192        |
+| 0.062         | 1.4085  | 100  | 0.1065          | 0.8361   | 0.8630        | 0.9638           | 0.9833              | 0.6084        | 0.9972             | 0.9720         | 0.5922   | 0.9441        |
+| 0.1757        | 2.1127  | 150  | 0.1157          | 0.8551   | 0.8896        | 0.9617           | 0.9803              | 0.7065        | 0.9820             | 0.9484         | 0.6731   | 0.9438        |
+| 0.0872        | 2.8169  | 200  | 0.0446          | 0.9302   | 0.9539        | 0.9844           | 0.9938              | 0.8760        | 0.9920             | 0.9846         | 0.8282   | 0.9777        |
+| 0.0336        | 3.5211  | 250  | 0.0338          | 0.9469   | 0.9751        | 0.9877           | 0.9913              | 0.9431        | 0.9910             | 0.9857         | 0.8719   | 0.9831        |
+| 0.0417        | 4.2254  | 300  | 0.0488          | 0.9281   | 0.9820        | 0.9830           | 0.9941              | 0.9765        | 0.9753             | 0.9877         | 0.8233   | 0.9732        |
+| 0.0273        | 4.9296  | 350  | 0.0295          | 0.9516   | 0.9628        | 0.9892           | 0.9952              | 0.8960        | 0.9973             | 0.9895         | 0.8819   | 0.9835        |
+| 0.0249        | 5.6338  | 400  | 0.0228          | 0.9627   | 0.9807        | 0.9913           | 0.9916              | 0.9544        | 0.9960             | 0.9890         | 0.9112   | 0.9879        |
+| 0.0247        | 6.3380  | 450  | 0.0234          | 0.9642   | 0.9886        | 0.9915           | 0.9919              | 0.9814        | 0.9925             | 0.9894         | 0.9151   | 0.9881        |
+| 0.0219        | 7.0423  | 500  | 0.0220          | 0.9656   | 0.9768        | 0.9920           | 0.9943              | 0.9386        | 0.9975             | 0.9908         | 0.9178   | 0.9882        |
+| 0.0172        | 7.7465  | 550  | 0.0206          | 0.9672   | 0.9888        | 0.9923           | 0.9951              | 0.9792        | 0.9919             | 0.9913         | 0.9215   | 0.9888        |
+| 0.018         | 8.4507  | 600  | 0.0169          | 0.9747   | 0.9859        | 0.9937           | 0.9944              | 0.9665        | 0.9969             | 0.9910         | 0.9420   | 0.9911        |
+| 0.0152        | 9.1549  | 650  | 0.0180          | 0.9726   | 0.9856        | 0.9932           | 0.9968              | 0.9659        | 0.9942             | 0.9909         | 0.9366   | 0.9902        |
+| 0.016         | 9.8592  | 700  | 0.0180          | 0.9729   | 0.9877        | 0.9936           | 0.9955              | 0.9726        | 0.9949             | 0.9917         | 0.9360   | 0.9909        |
+| 0.0132        | 10.5634 | 750  | 0.0169          | 0.9746   | 0.9872        | 0.9938           | 0.9944              | 0.9708        | 0.9965             | 0.9914         | 0.9410   | 0.9913        |
+| 0.0115        | 11.2676 | 800  | 0.0156          | 0.9761   | 0.9898        | 0.9941           | 0.9952              | 0.9789        | 0.9954             | 0.9920         | 0.9446   | 0.9917        |
+| 0.0143        | 11.9718 | 850  | 0.0155          | 0.9765   | 0.9895        | 0.9943           | 0.9962              | 0.9772        | 0.9952             | 0.9923         | 0.9452   | 0.9920        |
+| 0.0106        | 12.6761 | 900  | 0.0146          | 0.9778   | 0.9898        | 0.9946           | 0.9959              | 0.9777        | 0.9959             | 0.9924         | 0.9485   | 0.9925        |
+| 0.0106        | 13.3803 | 950  | 0.0146          | 0.9780   | 0.9888        | 0.9947           | 0.9967              | 0.9736        | 0.9959             | 0.9923         | 0.9490   | 0.9928        |
+| 0.0068        | 14.0845 | 1000 | 0.0147          | 0.9784   | 0.9883        | 0.9947           | 0.9966              | 0.9718        | 0.9964             | 0.9924         | 0.9501   | 0.9928        |
+| 0.0115        | 14.7887 | 1050 | 0.0163          | 0.9759   | 0.9901        | 0.9942           | 0.9958              | 0.9795        | 0.9950             | 0.9925         | 0.9436   | 0.9917        |
+| 0.0099        | 15.4930 | 1100 | 0.0150          | 0.9788   | 0.9887        | 0.9948           | 0.9958              | 0.9735        | 0.9969             | 0.9926         | 0.9509   | 0.9927        |
+### 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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