Push model using huggingface_hub.

README.md

@@ -1,105 +1,55 @@
 ---
-library_name: transformers
+library_name: segmentation-models-pytorch
+license: mit
+pipeline_tag: image-segmentation
 tags:
-- generated_from_trainer
-model-index:
-- name: unet-efficientnet-b3-malaria-trophozoites-wbc
-  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. -->
-
-# unet-efficientnet-b3-malaria-trophozoites-wbc
-
-This model was trained from scratch on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1498
-
-## 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 OptimizerNames.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: 50
-
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss |
-|:-------------:|:-----:|:-----:|:---------------:|
-| No log        | 1.0   | 484   | 0.1881          |
-| 0.6202        | 2.0   | 968   | 0.1806          |
-| 0.1542        | 3.0   | 1452  | 0.1813          |
-| 0.1496        | 4.0   | 1936  | 0.1782          |
-| 0.1417        | 5.0   | 2420  | 0.1692          |
-| 0.1435        | 6.0   | 2904  | 0.1737          |
-| 0.1388        | 7.0   | 3388  | 0.1655          |
-| 0.1369        | 8.0   | 3872  | 0.1672          |
-| 0.1359        | 9.0   | 4356  | 0.1630          |
-| 0.1377        | 10.0  | 4840  | 0.1619          |
-| 0.136         | 11.0  | 5324  | 0.1662          |
-| 0.1309        | 12.0  | 5808  | 0.2284          |
-| 0.1321        | 13.0  | 6292  | 0.1591          |
-| 0.1295        | 14.0  | 6776  | 0.1597          |
-| 0.1308        | 15.0  | 7260  | 0.1802          |
-| 0.1308        | 16.0  | 7744  | 0.1629          |
-| 0.1304        | 17.0  | 8228  | 0.1576          |
-| 0.1298        | 18.0  | 8712  | 0.1658          |
-| 0.1262        | 19.0  | 9196  | 0.1542          |
-| 0.1267        | 20.0  | 9680  | 0.1594          |
-| 0.1282        | 21.0  | 10164 | 0.1560          |
-| 0.1259        | 22.0  | 10648 | 0.1605          |
-| 0.1301        | 23.0  | 11132 | 0.1538          |
-| 0.124         | 24.0  | 11616 | 0.1535          |
-| 0.1241        | 25.0  | 12100 | 0.1566          |
-| 0.1237        | 26.0  | 12584 | 0.1554          |
-| 0.1291        | 27.0  | 13068 | 0.1531          |
-| 0.1233        | 28.0  | 13552 | 0.1543          |
-| 0.1235        | 29.0  | 14036 | 0.1538          |
-| 0.1247        | 30.0  | 14520 | 0.1527          |
-| 0.1255        | 31.0  | 15004 | 0.1622          |
-| 0.1255        | 32.0  | 15488 | 0.1549          |
-| 0.1236        | 33.0  | 15972 | 0.1527          |
-| 0.1246        | 34.0  | 16456 | 0.1522          |
-| 0.1233        | 35.0  | 16940 | 0.1535          |
-| 0.1237        | 36.0  | 17424 | 0.1524          |
-| 0.1252        | 37.0  | 17908 | 0.1510          |
-| 0.1201        | 38.0  | 18392 | 0.1514          |
-| 0.1263        | 39.0  | 18876 | 0.1521          |
-| 0.1221        | 40.0  | 19360 | 0.1525          |
-| 0.1213        | 41.0  | 19844 | 0.1514          |
-| 0.1193        | 42.0  | 20328 | 0.1504          |
-| 0.1216        | 43.0  | 20812 | 0.1502          |
-| 0.1223        | 44.0  | 21296 | 0.1506          |
-| 0.1198        | 45.0  | 21780 | 0.1508          |
-| 0.1208        | 46.0  | 22264 | 0.1500          |
-| 0.1233        | 47.0  | 22748 | 0.1502          |
-| 0.1176        | 48.0  | 23232 | 0.1495          |
-| 0.1225        | 49.0  | 23716 | 0.1499          |
-| 0.1212        | 50.0  | 24200 | 0.1498          |
-
-
-### Framework versions
-
-- Transformers 4.48.3
-- Pytorch 2.5.1+cu124
-- Datasets 3.3.0
-- 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": "efficientnet-b3",
+    "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": "efficientnet-b3",
+  "encoder_weights": "imagenet",
+  "in_channels": 3
+}

model.safetensors

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