best_model.pt

README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: windowz_test-020525
+  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. -->
+
+# windowz_test-020525
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Model Preparation Time: 0.0011
+- Accuracy: 0.9920
+- F1: 0.9921
+- Iou: 0.9850
+- Contour Dice: 0.9925
+- Loss: 0.0221
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step   | Model Preparation Time |        | Dice   | Validation Loss |
+|:-------------:|:-----:|:------:|:----------------------:|:------:|:------:|:---------------:|
+| 0.4601        | 5.0   | 25630  | 0.0011                 | 0.9713 | 0.9889 | 0.0642          |
+| 0.4405        | 10.0  | 51260  | 0.0011                 | 0.9760 | 0.9904 | 0.0372          |
+| 0.3981        | 15.0  | 76890  | 0.0011                 | 0.9793 | 0.9932 | 0.0316          |
+| 0.361         | 20.0  | 102520 | 0.0011                 | 0.9830 | 0.9925 | 0.0340          |
+| 0.3978        | 25.0  | 128150 | 0.0011                 | 0.9814 | 0.9903 | 0.0309          |
+| 0.4293        | 30.0  | 153780 | 0.0011                 | 0.9835 | 0.9923 | 0.0259          |
+| 0.3522        | 35.0  | 179410 | 0.0011                 | 0.9855 | 0.9927 | 0.0269          |
+| 0.3718        | 40.0  | 205040 | 0.0011                 | 0.9842 | 0.9931 | 0.0265          |
+| 0.37          | 45.0  | 230670 | 0.0011                 | 0.9851 | 0.9931 | 0.0242          |
+| 0.3866        | 50.0  | 256300 | 0.0011                 | 0.984  | 0.9920 | 0.0255          |
+| 0.3416        | 55.0  | 281930 | 0.0011                 | 0.9848 | 0.9930 | 0.0249          |
+| 0.3461        | 60.0  | 307560 | 0.0011                 | 0.9850 | 0.9925 | 0.0221          |
+| 0.3509        | 65.0  | 333190 | 0.0011                 | 0.9842 | 0.9919 | 0.0240          |
+| 0.3616        | 70.0  | 358820 | 0.0011                 | 0.9847 | 0.9931 | 0.0226          |
+| 0.3406        | 75.0  | 384450 | 0.0011                 | 0.9840 | 0.9926 | 0.0237          |
+
+
+### Framework versions
+
+- Transformers 4.45.0
+- Pytorch 2.5.1+cu124
+- Datasets 2.21.0
+- Tokenizers 0.20.3

config.json

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+{
+  "architectures": [
+    "UNETForSegmentation"
+  ],
+  "dim": 224,
+  "hidden_act": "gelu",
+  "hidden_size": 256,
+  "img_size": 128,
+  "intermediate_size": 1024,
+  "is_causal": false,
+  "k": 2,
+  "model_type": "Unet",
+  "n_filts": 4,
+  "num_attention_heads": 8,
+  "num_channels": 3,
+  "num_classes": 3,
+  "num_hidden_layers": 6,
+  "num_layers": 2,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "t": 2,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.0"
+}

model.safetensors

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+oid sha256:738ea5419858bed148a57418341f7d3bec399bdec615a64971e5fdc34bc60d42
+size 2188724

training_args.bin

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+oid sha256:e8b3c407c0734d8bd648fd13263a8dae9a5fd14da6d831f3b27fd08534bab0a3
+size 5240