End of training

README.md

@@ -1,199 +1,137 @@
 ---
 library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-segments-sidewalk-2
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- 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.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-
-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
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-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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+<!-- 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-2
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5234
+- Mean Iou: 0.3126
+- Mean Accuracy: 0.3724
+- Overall Accuracy: 0.8581
+- Accuracy Unlabeled: nan
+- Accuracy Flat-road: 0.8770
+- Accuracy Flat-sidewalk: 0.9523
+- Accuracy Flat-crosswalk: 0.8485
+- Accuracy Flat-cyclinglane: 0.8272
+- Accuracy Flat-parkingdriveway: 0.4816
+- Accuracy Flat-railtrack: nan
+- Accuracy Flat-curb: 0.4909
+- Accuracy Human-person: 0.6546
+- Accuracy Human-rider: 0.0
+- Accuracy Vehicle-car: 0.9390
+- Accuracy Vehicle-truck: 0.0
+- Accuracy Vehicle-bus: nan
+- Accuracy Vehicle-tramtrain: 0.0
+- Accuracy Vehicle-motorcycle: 0.0
+- Accuracy Vehicle-bicycle: 0.3174
+- Accuracy Vehicle-caravan: 0.0
+- Accuracy Vehicle-cartrailer: 0.0
+- Accuracy Construction-building: 0.8992
+- Accuracy Construction-door: 0.0
+- Accuracy Construction-wall: 0.4759
+- Accuracy Construction-fenceguardrail: 0.5201
+- Accuracy Construction-bridge: 0.0
+- Accuracy Construction-tunnel: nan
+- Accuracy Construction-stairs: 0.0
+- Accuracy Object-pole: 0.2217
+- Accuracy Object-trafficsign: 0.0
+- Accuracy Object-trafficlight: 0.0
+- Accuracy Nature-vegetation: 0.9260
+- Accuracy Nature-terrain: 0.8821
+- Accuracy Sky: 0.9643
+- Accuracy Void-ground: 0.0
+- Accuracy Void-dynamic: 0.0
+- Accuracy Void-static: 0.2678
+- Accuracy Void-unclear: 0.0
+- Iou Unlabeled: nan
+- Iou Flat-road: 0.7316
+- Iou Flat-sidewalk: 0.8854
+- Iou Flat-crosswalk: 0.7310
+- Iou Flat-cyclinglane: 0.7641
+- Iou Flat-parkingdriveway: 0.3722
+- Iou Flat-railtrack: nan
+- Iou Flat-curb: 0.3550
+- Iou Human-person: 0.4225
+- Iou Human-rider: 0.0
+- Iou Vehicle-car: 0.7847
+- Iou Vehicle-truck: 0.0
+- Iou Vehicle-bus: nan
+- Iou Vehicle-tramtrain: 0.0
+- Iou Vehicle-motorcycle: 0.0
+- Iou Vehicle-bicycle: 0.2941
+- Iou Vehicle-caravan: 0.0
+- Iou Vehicle-cartrailer: 0.0
+- Iou Construction-building: 0.7100
+- Iou Construction-door: 0.0
+- Iou Construction-wall: 0.3331
+- Iou Construction-fenceguardrail: 0.3969
+- Iou Construction-bridge: 0.0
+- Iou Construction-tunnel: nan
+- Iou Construction-stairs: 0.0
+- Iou Object-pole: 0.1649
+- Iou Object-trafficsign: 0.0
+- Iou Object-trafficlight: 0.0
+- Iou Nature-vegetation: 0.8508
+- Iou Nature-terrain: 0.7558
+- Iou Sky: 0.9201
+- Iou Void-ground: 0.0
+- Iou Void-dynamic: 0.0
+- Iou Void-static: 0.2191
+- Iou Void-unclear: 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- 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: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
+| 0.522         | 10.0  | 500  | 0.5791          | 0.2833   | 0.3397        | 0.8440           | nan                | 0.8636             | 0.9512                 | 0.8602                  | 0.7964                    | 0.4563                        | nan                     | 0.4273             | 0.4433                | 0.0                  | 0.9362               | 0.0                    | nan                  | 0.0                        | 0.0                         | 0.0                      | 0.0                      | 0.0                         | 0.8736                         | 0.0                        | 0.5360                     | 0.3822                               | 0.0                          | nan                          | 0.0                          | 0.1408               | 0.0                         | 0.0                          | 0.9296                     | 0.8815                  | 0.9592       | 0.0                  | 0.0                   | 0.0930               | 0.0                   | nan           | 0.7130        | 0.8766            | 0.7296             | 0.7177               | 0.3374                   | nan                | 0.3184        | 0.3476           | 0.0             | 0.7343          | 0.0               | nan             | 0.0                   | 0.0                    | 0.0                 | 0.0                 | 0.0                    | 0.6853                    | 0.0                   | 0.3241                | 0.3188                          | 0.0                     | nan                     | 0.0                     | 0.1067          | 0.0                    | 0.0                     | 0.8373                | 0.7432             | 0.9067  | 0.0             | 0.0              | 0.0842          | 0.0              |
+| 0.3419        | 20.0  | 1000 | 0.5479          | 0.2940   | 0.3564        | 0.8488           | nan                | 0.8365             | 0.9463                 | 0.8573                  | 0.8328                    | 0.4997                        | nan                     | 0.4725             | 0.6224                | 0.0                  | 0.9380               | 0.0                    | nan                  | 0.0                        | 0.0                         | 0.0164                   | 0.0                      | 0.0                         | 0.8911                         | 0.0                        | 0.4705                     | 0.5028                               | 0.0                          | nan                          | 0.0                          | 0.1982               | 0.0                         | 0.0                          | 0.9274                     | 0.8762                  | 0.9628       | 0.0                  | 0.0                   | 0.1976               | 0.0                   | nan           | 0.7062        | 0.8809            | 0.7043             | 0.7137               | 0.3615                   | nan                | 0.3403        | 0.4065           | 0.0             | 0.7574          | 0.0               | nan             | 0.0                   | 0.0                    | 0.0164              | 0.0                 | 0.0                    | 0.7023                    | 0.0                   | 0.3209                | 0.3770                          | 0.0                     | nan                     | 0.0                     | 0.1429          | 0.0                    | 0.0                     | 0.8473                | 0.7526             | 0.9139  | 0.0             | 0.0              | 0.1701          | 0.0              |
+| 0.3486        | 30.0  | 1500 | 0.5321          | 0.3037   | 0.3650        | 0.8542           | nan                | 0.8657             | 0.9479                 | 0.8389                  | 0.8365                    | 0.4912                        | nan                     | 0.4700             | 0.6601                | 0.0                  | 0.9401               | 0.0                    | nan                  | 0.0                        | 0.0                         | 0.1585                   | 0.0                      | 0.0                         | 0.8840                         | 0.0                        | 0.4615                     | 0.5186                               | 0.0                          | nan                          | 0.0                          | 0.2152               | 0.0                         | 0.0                          | 0.9337                     | 0.8777                  | 0.9651       | 0.0                  | 0.0                   | 0.2518               | 0.0                   | nan           | 0.7256        | 0.8839            | 0.7103             | 0.7474               | 0.3673                   | nan                | 0.3427        | 0.4156           | 0.0             | 0.7694          | 0.0               | nan             | 0.0                   | 0.0                    | 0.1545              | 0.0                 | 0.0                    | 0.7056                    | 0.0                   | 0.3170                | 0.3888                          | 0.0                     | nan                     | 0.0                     | 0.1569          | 0.0                    | 0.0                     | 0.8487                | 0.7562             | 0.9172  | 0.0             | 0.0              | 0.2068          | 0.0              |
+| 0.405         | 40.0  | 2000 | 0.5256          | 0.3101   | 0.3696        | 0.8569           | nan                | 0.8635             | 0.9520                 | 0.8452                  | 0.8387                    | 0.4748                        | nan                     | 0.4860             | 0.6552                | 0.0                  | 0.9395               | 0.0                    | nan                  | 0.0                        | 0.0                         | 0.2844                   | 0.0                      | 0.0                         | 0.9020                         | 0.0                        | 0.4572                     | 0.5207                               | 0.0                          | nan                          | 0.0                          | 0.2120               | 0.0                         | 0.0                          | 0.9275                     | 0.8817                  | 0.9647       | 0.0                  | 0.0                   | 0.2518               | 0.0                   | nan           | 0.7266        | 0.8842            | 0.7331             | 0.7556               | 0.3654                   | nan                | 0.3504        | 0.4206           | 0.0             | 0.7803          | 0.0               | nan             | 0.0                   | 0.0                    | 0.2665              | 0.0                 | 0.0                    | 0.7098                    | 0.0                   | 0.3327                | 0.3923                          | 0.0                     | nan                     | 0.0                     | 0.1603          | 0.0                    | 0.0                     | 0.8511                | 0.7559             | 0.9196  | 0.0             | 0.0              | 0.2095          | 0.0              |
+| 0.3626        | 50.0  | 2500 | 0.5234          | 0.3126   | 0.3724        | 0.8581           | nan                | 0.8770             | 0.9523                 | 0.8485                  | 0.8272                    | 0.4816                        | nan                     | 0.4909             | 0.6546                | 0.0                  | 0.9390               | 0.0                    | nan                  | 0.0                        | 0.0                         | 0.3174                   | 0.0                      | 0.0                         | 0.8992                         | 0.0                        | 0.4759                     | 0.5201                               | 0.0                          | nan                          | 0.0                          | 0.2217               | 0.0                         | 0.0                          | 0.9260                     | 0.8821                  | 0.9643       | 0.0                  | 0.0                   | 0.2678               | 0.0                   | nan           | 0.7316        | 0.8854            | 0.7310             | 0.7641               | 0.3722                   | nan                | 0.3550        | 0.4225           | 0.0             | 0.7847          | 0.0               | nan             | 0.0                   | 0.0                    | 0.2941              | 0.0                 | 0.0                    | 0.7100                    | 0.0                   | 0.3331                | 0.3969                          | 0.0                     | nan                     | 0.0                     | 0.1649          | 0.0                    | 0.0                     | 0.8508                | 0.7558             | 0.9201  | 0.0             | 0.0              | 0.2191          | 0.0              |
+
+
+### Framework versions
+
+- Transformers 4.53.0
+- Pytorch 2.7.1+cu126
+- Datasets 3.6.0
+- Tokenizers 0.21.2

config.json

@@ -0,0 +1,143 @@
+{
+  "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": "flat-road",
+    "2": "flat-sidewalk",
+    "3": "flat-crosswalk",
+    "4": "flat-cyclinglane",
+    "5": "flat-parkingdriveway",
+    "6": "flat-railtrack",
+    "7": "flat-curb",
+    "8": "human-person",
+    "9": "human-rider",
+    "10": "vehicle-car",
+    "11": "vehicle-truck",
+    "12": "vehicle-bus",
+    "13": "vehicle-tramtrain",
+    "14": "vehicle-motorcycle",
+    "15": "vehicle-bicycle",
+    "16": "vehicle-caravan",
+    "17": "vehicle-cartrailer",
+    "18": "construction-building",
+    "19": "construction-door",
+    "20": "construction-wall",
+    "21": "construction-fenceguardrail",
+    "22": "construction-bridge",
+    "23": "construction-tunnel",
+    "24": "construction-stairs",
+    "25": "object-pole",
+    "26": "object-trafficsign",
+    "27": "object-trafficlight",
+    "28": "nature-vegetation",
+    "29": "nature-terrain",
+    "30": "sky",
+    "31": "void-ground",
+    "32": "void-dynamic",
+    "33": "void-static",
+    "34": "void-unclear"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "construction-bridge": 22,
+    "construction-building": 18,
+    "construction-door": 19,
+    "construction-fenceguardrail": 21,
+    "construction-stairs": 24,
+    "construction-tunnel": 23,
+    "construction-wall": 20,
+    "flat-crosswalk": 3,
+    "flat-curb": 7,
+    "flat-cyclinglane": 4,
+    "flat-parkingdriveway": 5,
+    "flat-railtrack": 6,
+    "flat-road": 1,
+    "flat-sidewalk": 2,
+    "human-person": 8,
+    "human-rider": 9,
+    "nature-terrain": 29,
+    "nature-vegetation": 28,
+    "object-pole": 25,
+    "object-trafficlight": 27,
+    "object-trafficsign": 26,
+    "sky": 30,
+    "unlabeled": 0,
+    "vehicle-bicycle": 15,
+    "vehicle-bus": 12,
+    "vehicle-car": 10,
+    "vehicle-caravan": 16,
+    "vehicle-cartrailer": 17,
+    "vehicle-motorcycle": 14,
+    "vehicle-tramtrain": 13,
+    "vehicle-truck": 11,
+    "void-dynamic": 32,
+    "void-ground": 31,
+    "void-static": 33,
+    "void-unclear": 34
+  },
+  "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": "4.53.0"
+}

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