Push model using huggingface_hub.

README.md

@@ -1,75 +1,55 @@
 ---
-library_name: transformers
+library_name: segmentation-models-pytorch
+license: mit
+pipeline_tag: image-segmentation
 tags:
-- generated_from_trainer
-model-index:
-- name: results
-  results: []
+- model_hub_mixin
+- pytorch_model_hub_mixin
+- segmentation-models-pytorch
+- semantic-segmentation
+- pytorch
+languages:
+- python
 ---
-
-<!-- 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. -->
-
-# results
-
-This model was trained from scratch on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0027
-
-## 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: 0.003
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- 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
-- num_epochs: 20
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| No log        | 1.0   | 359  | 0.0041          |
-| 0.0048        | 2.0   | 718  | 0.0032          |
-| 0.0044        | 3.0   | 1077 | 0.0031          |
-| 0.0044        | 4.0   | 1436 | 0.0030          |
-| 0.0041        | 5.0   | 1795 | 0.0031          |
-| 0.004         | 6.0   | 2154 | 0.0029          |
-| 0.004         | 7.0   | 2513 | 0.0029          |
-| 0.004         | 8.0   | 2872 | 0.0037          |
-| 0.0039        | 9.0   | 3231 | 0.0029          |
-| 0.0039        | 10.0  | 3590 | 0.0029          |
-| 0.0039        | 11.0  | 3949 | 0.0028          |
-| 0.0037        | 12.0  | 4308 | 0.0029          |
-| 0.0037        | 13.0  | 4667 | 0.0028          |
-| 0.0037        | 14.0  | 5026 | 0.0028          |
-| 0.0037        | 15.0  | 5385 | 0.0028          |
-| 0.0036        | 16.0  | 5744 | 0.0028          |
-| 0.0036        | 17.0  | 6103 | 0.0027          |
-| 0.0036        | 18.0  | 6462 | 0.0027          |
-| 0.0036        | 19.0  | 6821 | 0.0027          |
-| 0.0035        | 20.0  | 7180 | 0.0027          |
-
-
-### Framework versions
-
-- Transformers 4.48.2
-- Pytorch 2.6.0+cu124
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+# Unet Model Card
+
+Table of Contents:
+- [Load trained model](#load-trained-model)
+- [Model init parameters](#model-init-parameters)
+- [Model metrics](#model-metrics)
+- [Dataset](#dataset)
+
+## Load trained model
+```python
+import segmentation_models_pytorch as smp
+
+model = smp.from_pretrained("<save-directory-or-this-repo>")
+```
+
+## Model init parameters
+```python
+model_init_params = {
+    "encoder_name": "resnet34",
+    "encoder_depth": 5,
+    "encoder_weights": "imagenet",
+    "decoder_use_batchnorm": True,
+    "decoder_channels": (256, 128, 64, 32, 16),
+    "decoder_attention_type": None,
+    "in_channels": 3,
+    "classes": 2,
+    "activation": None,
+    "aux_params": None
+}
+```
+
+## Model metrics
+[More Information Needed]
+
+## Dataset
+Dataset name: [More Information Needed]
+
+## More Information
+- Library: https://github.com/qubvel/segmentation_models.pytorch
+- Docs: https://smp.readthedocs.io/en/latest/
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)

config.json

@@ -0,0 +1,19 @@
+{
+  "_model_class": "Unet",
+  "activation": null,
+  "aux_params": null,
+  "classes": 2,
+  "decoder_attention_type": null,
+  "decoder_channels": [
+    256,
+    128,
+    64,
+    32,
+    16
+  ],
+  "decoder_use_batchnorm": true,
+  "encoder_depth": 5,
+  "encoder_name": "resnet34",
+  "encoder_weights": "imagenet",
+  "in_channels": 3
+}

model.safetensors

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