End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: google/vit-base-patch16-224-in21k
+base_model: gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease
 tags:
 - generated_from_trainer
 metrics:
@@ -15,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # CGIAR
 
-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 None dataset.
+This model is a fine-tuned version of [gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease](https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8587
-- Accuracy: 0.6214
+- Loss: 0.7888
+- Accuracy: 0.6571
 
 ## Model description
 
@@ -44,47 +44,21 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 50
-- num_epochs: 30
+- num_epochs: 4
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 1.1422        | 1.0   | 652   | 1.1329          | 0.4645   |
-| 1.1314        | 2.0   | 1304  | 1.0424          | 0.5230   |
-| 1.115         | 3.0   | 1956  | 1.0801          | 0.4988   |
-| 1.0937        | 4.0   | 2608  | 1.0455          | 0.5069   |
-| 1.0764        | 5.0   | 3260  | 1.0277          | 0.5184   |
-| 1.0728        | 6.0   | 3912  | 1.0600          | 0.5236   |
-| 1.0629        | 7.0   | 4564  | 0.9992          | 0.5495   |
-| 1.0513        | 8.0   | 5216  | 1.0137          | 0.5483   |
-| 1.0415        | 9.0   | 5868  | 0.9810          | 0.5679   |
-| 1.023         | 10.0  | 6520  | 0.9669          | 0.5552   |
-| 1.0161        | 11.0  | 7172  | 0.9502          | 0.5717   |
-| 1.0137        | 12.0  | 7824  | 0.9645          | 0.5683   |
-| 1.0051        | 13.0  | 8476  | 0.9538          | 0.5508   |
-| 1.0049        | 14.0  | 9128  | 0.9380          | 0.5794   |
-| 0.9919        | 15.0  | 9780  | 0.9338          | 0.5865   |
-| 0.9908        | 16.0  | 10432 | 0.9196          | 0.5857   |
-| 0.9805        | 17.0  | 11084 | 0.9443          | 0.5585   |
-| 0.9738        | 18.0  | 11736 | 0.9224          | 0.5842   |
-| 0.9566        | 19.0  | 12388 | 0.9142          | 0.5867   |
-| 0.9511        | 20.0  | 13040 | 0.9038          | 0.6087   |
-| 0.9494        | 21.0  | 13692 | 0.9079          | 0.5919   |
-| 0.9462        | 22.0  | 14344 | 0.8822          | 0.6133   |
-| 0.9321        | 23.0  | 14996 | 0.8908          | 0.6009   |
-| 0.9375        | 24.0  | 15648 | 0.8813          | 0.6045   |
-| 0.9315        | 25.0  | 16300 | 0.8792          | 0.6093   |
-| 0.9195        | 26.0  | 16952 | 0.8740          | 0.6128   |
-| 0.9085        | 27.0  | 17604 | 0.8734          | 0.6110   |
-| 0.9086        | 28.0  | 18256 | 0.8639          | 0.6170   |
-| 0.9111        | 29.0  | 18908 | 0.8606          | 0.6208   |
-| 0.9061        | 30.0  | 19560 | 0.8587          | 0.6214   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.0123        | 1.0   | 652  | 0.8818          | 0.6178   |
+| 0.8619        | 2.0   | 1304 | 0.8398          | 0.6346   |
+| 0.8324        | 3.0   | 1956 | 0.8233          | 0.6366   |
+| 0.7872        | 4.0   | 2608 | 0.7888          | 0.6571   |
 
 
 ### Framework versions
 
-- Transformers 4.36.2
+- Transformers 4.37.1
 - Pytorch 2.0.0
-- Datasets 2.15.0
+- Datasets 2.16.1
 - Tokenizers 0.15.0