aholk/WindowzUNet-4class-ML
Browse files- README.md +72 -74
- config.json +24 -24
- model.safetensors +1 -1
- training_args.bin +2 -2
README.md
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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model-index:
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- name: windowz_ln_segment_122725
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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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# windowz_ln_segment_122725
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Model Preparation Time: 0.
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- Accuracy: 0.
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- F1: 0.
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- Iou: 0.
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- Per Class Metrics: {0: {'f1': 0.
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- Loss:
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 0.02
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### Training results
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| Training Loss | Epoch | Step | Model Preparation Time | | Class Metrics
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|:-------------:|:------:|:----:|:----------------------:|:------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|
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| No log | 0.0020 | 1 | 0.
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| No log | 0.0040 | 2 | 0.
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| No log | 0.0060 | 3 | 0.
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| No log | 0.0080 | 4 | 0.
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| No log | 0.0100 | 5 | 0.
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| No log | 0.0120 | 6 | 0.
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| No log | 0.0140 | 7 | 0.
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| No log | 0.0160 | 8 | 0.
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- Datasets 2.21.0
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- Tokenizers 0.21.4
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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model-index:
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- name: windowz_ln_segment_122725
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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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# windowz_ln_segment_122725
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Model Preparation Time: 0.0016
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- Accuracy: 0.5423
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- F1: 0.1626
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- Iou: 0.0940
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- Per Class Metrics: {0: {'f1': 0.22376, 'iou': 0.12597, 'accuracy': 0.97977}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08736, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}}
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- Loss: 4.0417
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 0.02
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### Training results
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| Training Loss | Epoch | Step | Model Preparation Time | | Class Metrics | Validation Loss |
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|:-------------:|:------:|:----:|:----------------------:|:------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|
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| No log | 0.0020 | 1 | 0.0016 | 0.0704 | {0: {'f1': 0.05871, 'iou': 0.03024, 'accuracy': 0.60575}, 1: {'f1': 0.00033, 'iou': 0.00016, 'accuracy': 0.93957}, 2: {'f1': 0.08581, 'iou': 0.04483, 'accuracy': 0.09702}, 3: {'f1': 0.3423, 'iou': 0.20649, 'accuracy': 0.21595}} | 4.0518 |
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| No log | 0.0040 | 2 | 0.0016 | 0.0706 | {0: {'f1': 0.05996, 'iou': 0.03091, 'accuracy': 0.61825}, 1: {'f1': 5e-05, 'iou': 2e-05, 'accuracy': 0.93958}, 2: {'f1': 0.0864, 'iou': 0.04515, 'accuracy': 0.09174}, 3: {'f1': 0.34182, 'iou': 0.20614, 'accuracy': 0.21372}} | 4.0489 |
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| No log | 0.0060 | 3 | 0.0016 | 0.0909 | {0: {'f1': 0.20415, 'iou': 0.11368, 'accuracy': 0.9479}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08736, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}} | 4.0433 |
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| No log | 0.0080 | 4 | 0.0016 | 0.0936 | {0: {'f1': 0.22142, 'iou': 0.12449, 'accuracy': 0.97471}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08736, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}} | 4.0420 |
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| No log | 0.0100 | 5 | 0.0016 | 0.0940 | {0: {'f1': 0.22376, 'iou': 0.12597, 'accuracy': 0.97977}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08736, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}} | 4.0417 |
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| No log | 0.0120 | 6 | 0.0016 | 0.0965 | {0: {'f1': 0.23961, 'iou': 0.13611, 'accuracy': 0.9772}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08736, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}} | 4.0431 |
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| No log | 0.0140 | 7 | 0.0016 | 0.0968 | {0: {'f1': 0.24112, 'iou': 0.13708, 'accuracy': 0.9754}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08736, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}} | 4.0472 |
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| No log | 0.0160 | 8 | 0.0016 | 0.096 | {0: {'f1': 0.23636, 'iou': 0.13402, 'accuracy': 0.9745}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.93958}, 2: {'f1': 0.08737, 'iou': 0.04568, 'accuracy': 0.04568}, 3: {'f1': 0.33929, 'iou': 0.2043, 'accuracy': 0.2043}} | 4.0510 |
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### Framework versions
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- Transformers 4.47.1
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- Pytorch 2.9.1+cpu
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- Datasets 4.5.0
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- Tokenizers 0.21.4
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config.json
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{
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"architectures": [
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"UNETForSegmentation"
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],
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"dim": 224,
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"hidden_act": "gelu",
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"hidden_size": 256,
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"img_size": 128,
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"intermediate_size": 1024,
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"is_causal": false,
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"k": 2,
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"model_type": "Unet",
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"n_filts": 4,
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"num_attention_heads": 8,
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"num_channels": 3,
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"num_classes": 4,
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"num_hidden_layers": 6,
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"num_layers": 2,
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"patch_size": 16,
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"problem_type": "multi_label_classification",
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"t": 2,
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"torch_dtype": "float32",
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"transformers_version": "4.47.1"
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}
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{
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"architectures": [
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"UNETForSegmentation"
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],
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"dim": 224,
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"hidden_act": "gelu",
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"hidden_size": 256,
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"img_size": 128,
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"intermediate_size": 1024,
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"is_causal": false,
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"k": 2,
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"model_type": "Unet",
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"n_filts": 4,
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"num_attention_heads": 8,
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"num_channels": 3,
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"num_classes": 4,
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"num_hidden_layers": 6,
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"num_layers": 2,
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"patch_size": 16,
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"problem_type": "multi_label_classification",
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"t": 2,
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"torch_dtype": "float32",
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"transformers_version": "4.47.1"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2188760
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size
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size 5777
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