End of training

README.md

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+---
+license: other
+base_model: nvidia/mit-b4
+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-b4](https://huggingface.co/nvidia/mit-b4) on the segments/sidewalk-semantic dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0243
+- Mean Iou: 0.9582
+- Mean Accuracy: 0.9792
+- Overall Accuracy: 0.9965
+- Accuracy Unlabeled: 0.9981
+- Accuracy Numero: 0.9603
+- Iou Unlabeled: 0.9963
+- Iou Numero: 0.9200
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Numero | Iou Unlabeled | Iou Numero |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:----------:|
+| 0.1406        | 5.0   | 20   | 0.1672          | 0.7389   | 0.7497        | 0.9790           | 1.0000             | 0.4994          | 0.9785        | 0.4993     |
+| 0.045         | 10.0  | 40   | 0.0498          | 0.9398   | 0.9476        | 0.9951           | 0.9994             | 0.8958          | 0.9949        | 0.8846     |
+| 0.0361        | 15.0  | 60   | 0.0296          | 0.9575   | 0.9811        | 0.9964           | 0.9978             | 0.9643          | 0.9963        | 0.9187     |
+| 0.026         | 20.0  | 80   | 0.0243          | 0.9582   | 0.9792        | 0.9965           | 0.9981             | 0.9603          | 0.9963        | 0.9200     |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "nvidia/mit-b4",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    8,
+    27,
+    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": "unlabeled",
+    "1": "numero"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "numero": 1,
+    "unlabeled": 0
+  },
+  "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": "4.35.2"
+}

model.safetensors

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runs/Nov22_09-30-16_caf495897c56/events.out.tfevents.1700645439.caf495897c56.266.0

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training_args.bin

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