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segformer-b5-cityscapes-finetuned-coastTrain-grCoastline

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  1. README.md +98 -198
  2. config.json +88 -0
  3. model.safetensors +3 -0
  4. runs/Mar06_13-07-12_537a3af3d11f/events.out.tfevents.1709730436.537a3af3d11f.26.0 +3 -0
  5. training_args.bin +3 -0
README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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-
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- ## Model Details
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-
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- ### Model Description
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- 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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-
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- ## Uses
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-
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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. -->
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-
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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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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- ## Bias, Risks, and Limitations
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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.
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-
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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-
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- ## Training Details
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- ### Training Data
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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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-
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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 -->
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- #### 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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- ## 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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- #### 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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- 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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- - **Hardware Type:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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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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- ## Glossary [optional]
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-
 
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  ---
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+ license: other
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+ base_model: peldrak/segformer-b5-cityscapes-finetuned-coastTrain
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b5-cityscapes-finetuned-coastTrain-grCoastline
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+ results: []
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # segformer-b5-cityscapes-finetuned-coastTrain-grCoastline
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+
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+ This model is a fine-tuned version of [peldrak/segformer-b5-cityscapes-finetuned-coastTrain](https://huggingface.co/peldrak/segformer-b5-cityscapes-finetuned-coastTrain) on the peldrak/grCoastline_512 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2102
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+ - Mean Iou: 0.7609
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+ - Mean Accuracy: 0.8362
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+ - Overall Accuracy: 0.9377
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+ - Accuracy Water: 0.9647
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+ - Accuracy Whitewater: 0.4711
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+ - Accuracy Sediment: 0.9196
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+ - Accuracy Other Natural Terrain: 0.7165
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+ - Accuracy Vegetation: 0.9431
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+ - Accuracy Development: 0.8401
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+ - Accuracy Unknown: 0.9979
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+ - Iou Water: 0.9257
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+ - Iou Whitewater: 0.4024
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+ - Iou Sediment: 0.8495
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+ - Iou Other Natural Terrain: 0.6281
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+ - Iou Vegetation: 0.8602
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+ - Iou Development: 0.6642
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+ - Iou Unknown: 0.9965
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+ - F1 Score: 0.9367
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
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+ | 0.2282 | 0.24 | 20 | 0.3888 | 0.5888 | 0.6807 | 0.8840 | 0.8811 | 0.0154 | 0.8670 | 0.3235 | 0.9707 | 0.7083 | 0.9990 | 0.8552 | 0.0135 | 0.7307 | 0.3052 | 0.7768 | 0.4547 | 0.9857 | 0.8725 |
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+ | 0.5683 | 0.49 | 40 | 0.2252 | 0.6632 | 0.7209 | 0.9264 | 0.9521 | 0.0387 | 0.9092 | 0.7579 | 0.9473 | 0.4427 | 0.9984 | 0.9405 | 0.0374 | 0.7823 | 0.6278 | 0.8414 | 0.4179 | 0.9951 | 0.9231 |
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+ | 0.3519 | 0.73 | 60 | 0.2345 | 0.6851 | 0.7534 | 0.9268 | 0.9607 | 0.0499 | 0.8663 | 0.6494 | 0.9576 | 0.7916 | 0.9986 | 0.9439 | 0.0499 | 0.8103 | 0.5751 | 0.8326 | 0.5900 | 0.9938 | 0.9245 |
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+ | 0.5472 | 0.98 | 80 | 0.2040 | 0.6776 | 0.7442 | 0.9218 | 0.9759 | 0.1349 | 0.8451 | 0.8238 | 0.9061 | 0.5263 | 0.9973 | 0.9316 | 0.1312 | 0.7764 | 0.5805 | 0.8485 | 0.4802 | 0.9944 | 0.9216 |
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+ | 0.7595 | 1.22 | 100 | 0.1882 | 0.7138 | 0.7855 | 0.9402 | 0.9571 | 0.0682 | 0.9318 | 0.7405 | 0.9428 | 0.8590 | 0.9986 | 0.9415 | 0.0670 | 0.8363 | 0.6783 | 0.8666 | 0.6121 | 0.9946 | 0.9393 |
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+ | 0.1791 | 1.46 | 120 | 0.2253 | 0.6703 | 0.7359 | 0.9203 | 0.9715 | 0.1066 | 0.7843 | 0.8978 | 0.9042 | 0.4902 | 0.9968 | 0.9436 | 0.1051 | 0.7418 | 0.5885 | 0.8501 | 0.4688 | 0.9944 | 0.9207 |
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+ | 0.099 | 1.71 | 140 | 0.1711 | 0.7070 | 0.7671 | 0.9390 | 0.9642 | 0.0155 | 0.9068 | 0.7598 | 0.9466 | 0.7778 | 0.9993 | 0.9354 | 0.0154 | 0.8536 | 0.6604 | 0.8601 | 0.6310 | 0.9929 | 0.9376 |
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+ | 0.3454 | 1.95 | 160 | 0.1869 | 0.7123 | 0.7751 | 0.9355 | 0.9626 | 0.0939 | 0.8778 | 0.7857 | 0.9359 | 0.7710 | 0.9991 | 0.9242 | 0.0909 | 0.8308 | 0.6435 | 0.8586 | 0.6439 | 0.9942 | 0.9347 |
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+ | 0.3765 | 2.2 | 180 | 0.2579 | 0.6727 | 0.7470 | 0.9147 | 0.9648 | 0.2392 | 0.8649 | 0.8125 | 0.8872 | 0.4622 | 0.9980 | 0.9172 | 0.2135 | 0.7302 | 0.5672 | 0.8387 | 0.4467 | 0.9955 | 0.9143 |
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+ | 0.1494 | 2.44 | 200 | 0.1862 | 0.7462 | 0.8087 | 0.9383 | 0.9556 | 0.3380 | 0.9123 | 0.7169 | 0.9600 | 0.7786 | 0.9995 | 0.9175 | 0.3066 | 0.8442 | 0.6488 | 0.8645 | 0.6482 | 0.9933 | 0.9367 |
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+ | 0.0858 | 2.68 | 220 | 0.2327 | 0.6835 | 0.7364 | 0.9258 | 0.9536 | 0.1393 | 0.8564 | 0.6946 | 0.9776 | 0.5357 | 0.9977 | 0.9356 | 0.1286 | 0.7870 | 0.5981 | 0.8339 | 0.5054 | 0.9957 | 0.9227 |
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+ | 0.2183 | 2.93 | 240 | 0.2064 | 0.7037 | 0.7744 | 0.9265 | 0.9629 | 0.2369 | 0.8597 | 0.8812 | 0.8947 | 0.5870 | 0.9982 | 0.9361 | 0.2075 | 0.7745 | 0.6237 | 0.8487 | 0.5412 | 0.9945 | 0.9271 |
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+ | 0.2847 | 3.17 | 260 | 0.1900 | 0.7415 | 0.8126 | 0.9384 | 0.9648 | 0.2936 | 0.9103 | 0.8383 | 0.9127 | 0.7703 | 0.9984 | 0.9267 | 0.2601 | 0.8488 | 0.6661 | 0.8642 | 0.6293 | 0.9949 | 0.9387 |
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+ | 0.2017 | 3.41 | 280 | 0.2369 | 0.7389 | 0.8151 | 0.9295 | 0.9689 | 0.4884 | 0.7658 | 0.8639 | 0.9304 | 0.6888 | 0.9994 | 0.9394 | 0.3980 | 0.7384 | 0.6180 | 0.8706 | 0.6128 | 0.9950 | 0.9301 |
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+ | 0.1149 | 3.66 | 300 | 0.2208 | 0.7379 | 0.7956 | 0.9378 | 0.9741 | 0.3777 | 0.8644 | 0.7810 | 0.9657 | 0.6090 | 0.9976 | 0.9322 | 0.3424 | 0.7988 | 0.6671 | 0.8745 | 0.5543 | 0.9959 | 0.9361 |
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+ | 0.1486 | 3.9 | 320 | 0.2257 | 0.7054 | 0.7787 | 0.9250 | 0.9668 | 0.1478 | 0.9145 | 0.8363 | 0.8638 | 0.7222 | 0.9993 | 0.9197 | 0.1436 | 0.8302 | 0.6078 | 0.8190 | 0.6220 | 0.9953 | 0.9256 |
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+ | 0.0889 | 4.15 | 340 | 0.1971 | 0.7383 | 0.7980 | 0.9385 | 0.9750 | 0.3057 | 0.9131 | 0.8671 | 0.9171 | 0.6115 | 0.9969 | 0.9276 | 0.2863 | 0.8502 | 0.6710 | 0.8657 | 0.5722 | 0.9953 | 0.9381 |
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+ | 0.1308 | 4.39 | 360 | 0.1887 | 0.7507 | 0.8048 | 0.9416 | 0.9659 | 0.3097 | 0.9215 | 0.7523 | 0.9584 | 0.7273 | 0.9982 | 0.9270 | 0.2915 | 0.8567 | 0.6637 | 0.8669 | 0.6533 | 0.9961 | 0.9402 |
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+ | 0.146 | 4.63 | 380 | 0.2034 | 0.7449 | 0.8091 | 0.9361 | 0.9645 | 0.3492 | 0.8155 | 0.8196 | 0.9473 | 0.7686 | 0.9992 | 0.9443 | 0.3206 | 0.7736 | 0.6454 | 0.8703 | 0.6646 | 0.9956 | 0.9360 |
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+ | 0.098 | 4.88 | 400 | 0.1943 | 0.7430 | 0.8007 | 0.9397 | 0.9688 | 0.3657 | 0.9452 | 0.7744 | 0.9473 | 0.6057 | 0.9975 | 0.9370 | 0.3337 | 0.8237 | 0.6772 | 0.8678 | 0.5655 | 0.9961 | 0.9381 |
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+ | 0.1064 | 5.12 | 420 | 0.1790 | 0.7668 | 0.8290 | 0.9447 | 0.9690 | 0.4338 | 0.9196 | 0.7875 | 0.9525 | 0.7420 | 0.9986 | 0.9347 | 0.3660 | 0.8587 | 0.6925 | 0.8725 | 0.6471 | 0.9961 | 0.9438 |
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+ | 0.0831 | 5.37 | 440 | 0.2016 | 0.7515 | 0.8160 | 0.9405 | 0.9703 | 0.4310 | 0.9266 | 0.9177 | 0.9073 | 0.5613 | 0.9980 | 0.9476 | 0.3823 | 0.8391 | 0.6967 | 0.8666 | 0.5317 | 0.9962 | 0.9402 |
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+ | 0.1538 | 5.61 | 460 | 0.1766 | 0.7492 | 0.8029 | 0.9444 | 0.9691 | 0.2995 | 0.9334 | 0.7742 | 0.9583 | 0.6873 | 0.9989 | 0.9317 | 0.2831 | 0.8541 | 0.6976 | 0.8755 | 0.6061 | 0.9962 | 0.9429 |
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+ | 0.1216 | 5.85 | 480 | 0.2057 | 0.7548 | 0.8214 | 0.9399 | 0.9677 | 0.4431 | 0.9097 | 0.9057 | 0.9113 | 0.6144 | 0.9976 | 0.9484 | 0.3868 | 0.8327 | 0.6881 | 0.8652 | 0.5658 | 0.9962 | 0.9400 |
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+ | 0.1182 | 6.1 | 500 | 0.2102 | 0.7609 | 0.8362 | 0.9377 | 0.9647 | 0.4711 | 0.9196 | 0.7165 | 0.9431 | 0.8401 | 0.9979 | 0.9257 | 0.4024 | 0.8495 | 0.6281 | 0.8602 | 0.6642 | 0.9965 | 0.9367 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.1
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+ - Pytorch 2.1.2
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.38.1"
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+ }
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