End of training

README.md

@@ -1,199 +1,135 @@
 ---
 library_name: transformers
-tags: []
+license: other
+base_model: nvidia/segformer-b2-finetuned-cityscapes-1024-1024
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: SegFormer_b2
+  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]
-- **Funded by [optional]:** [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
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### 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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-[More Information Needed]
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-### Downstream Use [optional]
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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-[More Information Needed]
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-## Training Details
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-### Training Data
-
-<!-- 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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-
-#### 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
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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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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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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-### Compute Infrastructure
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-## Citation [optional]
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-## Glossary [optional]
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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_b2
+
+This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b2-finetuned-cityscapes-1024-1024) on the Cityscapes dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.2516
+- eval_mean_iou: 0.3875
+- eval_mean_accuracy: 0.5066
+- eval_overall_accuracy: 0.9043
+- eval_accuracy_unlabeled: nan
+- eval_accuracy_ego vehicle: nan
+- eval_accuracy_rectification border: nan
+- eval_accuracy_out of roi: nan
+- eval_accuracy_static: nan
+- eval_accuracy_dynamic: nan
+- eval_accuracy_ground: nan
+- eval_accuracy_road: 0.9832
+- eval_accuracy_sidewalk: 0.8421
+- eval_accuracy_parking: nan
+- eval_accuracy_rail track: nan
+- eval_accuracy_building: 0.9158
+- eval_accuracy_wall: 0.0
+- eval_accuracy_fence: 0.0
+- eval_accuracy_guard rail: nan
+- eval_accuracy_bridge: nan
+- eval_accuracy_tunnel: nan
+- eval_accuracy_pole: 0.5362
+- eval_accuracy_polegroup: nan
+- eval_accuracy_traffic light: 0.5814
+- eval_accuracy_traffic sign: 0.7376
+- eval_accuracy_vegetation: 0.9188
+- eval_accuracy_terrain: 0.6737
+- eval_accuracy_sky: 0.9746
+- eval_accuracy_person: 0.7788
+- eval_accuracy_rider: 0.0
+- eval_accuracy_car: 0.9354
+- eval_accuracy_truck: 0.0
+- eval_accuracy_bus: 0.0
+- eval_accuracy_caravan: nan
+- eval_accuracy_trailer: nan
+- eval_accuracy_train: 0.0
+- eval_accuracy_motorcycle: 0.0
+- eval_accuracy_bicycle: 0.7472
+- eval_accuracy_license plate: nan
+- eval_iou_unlabeled: nan
+- eval_iou_ego vehicle: nan
+- eval_iou_rectification border: nan
+- eval_iou_out of roi: nan
+- eval_iou_static: 0.0
+- eval_iou_dynamic: nan
+- eval_iou_ground: nan
+- eval_iou_road: 0.9649
+- eval_iou_sidewalk: 0.7403
+- eval_iou_parking: nan
+- eval_iou_rail track: nan
+- eval_iou_building: 0.8430
+- eval_iou_wall: 0.0
+- eval_iou_fence: 0.0
+- eval_iou_guard rail: nan
+- eval_iou_bridge: nan
+- eval_iou_tunnel: nan
+- eval_iou_pole: 0.3619
+- eval_iou_polegroup: nan
+- eval_iou_traffic light: 0.4506
+- eval_iou_traffic sign: 0.5317
+- eval_iou_vegetation: 0.8647
+- eval_iou_terrain: 0.4610
+- eval_iou_sky: 0.8806
+- eval_iou_person: 0.5967
+- eval_iou_rider: 0.0
+- eval_iou_car: 0.8756
+- eval_iou_truck: 0.0
+- eval_iou_bus: 0.0
+- eval_iou_caravan: nan
+- eval_iou_trailer: nan
+- eval_iou_train: 0.0
+- eval_iou_motorcycle: 0.0
+- eval_iou_bicycle: 0.5665
+- eval_iou_license plate: 0.0
+- eval_runtime: 185.4692
+- eval_samples_per_second: 2.696
+- eval_steps_per_second: 0.674
+- epoch: 20.4301
+- step: 3800
+
+## 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: 0.0006
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- 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
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

config.json

@@ -0,0 +1,144 @@
+{
+  "_name_or_path": "nvidia/segformer-b2-finetuned-cityscapes-1024-1024",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    4,
+    6,
+    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": "ego vehicle",
+    "2": "rectification border",
+    "3": "out of roi",
+    "4": "static",
+    "5": "dynamic",
+    "6": "ground",
+    "7": "road",
+    "8": "sidewalk",
+    "9": "parking",
+    "10": "rail track",
+    "11": "building",
+    "12": "wall",
+    "13": "fence",
+    "14": "guard rail",
+    "15": "bridge",
+    "16": "tunnel",
+    "17": "pole",
+    "18": "polegroup",
+    "19": "traffic light",
+    "20": "traffic sign",
+    "21": "vegetation",
+    "22": "terrain",
+    "23": "sky",
+    "24": "person",
+    "25": "rider",
+    "26": "car",
+    "27": "truck",
+    "28": "bus",
+    "29": "caravan",
+    "30": "trailer",
+    "31": "train",
+    "32": "motorcycle",
+    "33": "bicycle",
+    "34": "license plate"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "bicycle": 33,
+    "bridge": 15,
+    "building": 11,
+    "bus": 28,
+    "car": 26,
+    "caravan": 29,
+    "dynamic": 5,
+    "ego vehicle": 1,
+    "fence": 13,
+    "ground": 6,
+    "guard rail": 14,
+    "license plate": 34,
+    "motorcycle": 32,
+    "out of roi": 3,
+    "parking": 9,
+    "person": 24,
+    "pole": 17,
+    "polegroup": 18,
+    "rail track": 10,
+    "rectification border": 2,
+    "rider": 25,
+    "road": 7,
+    "sidewalk": 8,
+    "sky": 23,
+    "static": 4,
+    "terrain": 22,
+    "traffic light": 19,
+    "traffic sign": 20,
+    "trailer": 30,
+    "train": 31,
+    "truck": 27,
+    "tunnel": 16,
+    "unlabeled": 0,
+    "vegetation": 21,
+    "wall": 12
+  },
+  "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": "4.47.1"
+}

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