update model card README.md

README.md

@@ -1 +1,80 @@
-Modelo de detección de campos con equipo de riego
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- f1
+model-index:
+- name: vit-base-riego
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: MaxP--agro_riego
+      split: train
+      args: MaxP--agro_riego
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9853801169590642
+---
+
+<!-- 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. -->
+
+# vit-base-riego
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0544
+- F1: 0.9854
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.0443        | 0.48  | 100  | 0.4737          | 0.8742 |
+| 0.0349        | 0.95  | 200  | 0.0774          | 0.9733 |
+| 0.003         | 1.43  | 300  | 0.0675          | 0.9794 |
+| 0.002         | 1.9   | 400  | 0.0674          | 0.9810 |
+| 0.0014        | 2.38  | 500  | 0.0431          | 0.9871 |
+| 0.0011        | 2.86  | 600  | 0.0544          | 0.9854 |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2