End of training

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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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-segments-sidewalk-oct-22
+  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-segments-sidewalk-oct-22
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the adsjnajefwnb/di dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1425
+- Mean Iou: 0.4608
+- Mean Accuracy: 0.6144
+- Overall Accuracy: 0.9524
+- Accuracy Unlabeled: nan
+- Accuracy Anqvan: nan
+- Accuracy Baohuxiang: nan
+- Accuracy Biaozhi: nan
+- Accuracy Dianlan: 0.9591
+- Accuracy Miehuoqi: 0.8841
+- Accuracy Zhaoming: 0.0
+- Iou Unlabeled: 0.0
+- Iou Anqvan: nan
+- Iou Baohuxiang: nan
+- Iou Biaozhi: nan
+- Iou Dianlan: 0.9591
+- Iou Miehuoqi: 0.8841
+- Iou Zhaoming: 0.0
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Use 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: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Anqvan | Accuracy Baohuxiang | Accuracy Biaozhi | Accuracy Dianlan | Accuracy Miehuoqi | Accuracy Zhaoming | Iou Unlabeled | Iou Anqvan | Iou Baohuxiang | Iou Biaozhi | Iou Dianlan | Iou Miehuoqi | Iou Zhaoming |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:----------------:|:----------------:|:-----------------:|:-----------------:|:-------------:|:----------:|:--------------:|:-----------:|:-----------:|:------------:|:------------:|
+| 1.5348        | 10.0  | 20   | 1.8080          | 0.3181   | 0.6370        | 0.9720           | nan                | nan             | nan                 | nan              | 0.9776           | 0.9334            | 0.0               | 0.0           | nan        | 0.0            | 0.0         | 0.9759      | 0.9328       | 0.0          |
+| 1.5003        | 20.0  | 40   | 1.5287          | 0.3865   | 0.6442        | 0.9726           | nan                | nan             | nan                 | nan              | 0.9773           | 0.9552            | 0.0               | 0.0           | nan        | nan            | 0.0         | 0.9773      | 0.9552       | 0.0          |
+| 1.4909        | 30.0  | 60   | 1.2543          | 0.4770   | 0.6359        | 0.9695           | nan                | nan             | nan                 | nan              | 0.9750           | 0.9328            | 0.0               | 0.0           | nan        | nan            | nan         | 0.9750      | 0.9328       | 0.0          |
+| 1.331         | 40.0  | 80   | 1.1463          | 0.4671   | 0.6229        | 0.9613           | nan                | nan             | nan                 | nan              | 0.9677           | 0.9009            | 0.0               | 0.0           | nan        | nan            | nan         | 0.9677      | 0.9009       | 0.0          |
+| 1.0843        | 50.0  | 100  | 1.1425          | 0.4608   | 0.6144        | 0.9524           | nan                | nan             | nan                 | nan              | 0.9591           | 0.8841            | 0.0               | 0.0           | nan        | nan            | nan         | 0.9591      | 0.8841       | 0.0          |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 1.12.1+cu113
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json

@@ -0,0 +1,88 @@
+{
+  "_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": "unlabeled",
+    "1": "anqvan",
+    "2": "baohuxiang",
+    "3": "biaozhi",
+    "4": "dianlan",
+    "5": "miehuoqi",
+    "6": "zhaoming"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "anqvan": 1,
+    "baohuxiang": 2,
+    "biaozhi": 3,
+    "dianlan": 4,
+    "miehuoqi": 5,
+    "unlabeled": 0,
+    "zhaoming": 6
+  },
+  "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": "4.46.3"
+}

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