Model save

README.md

@@ -18,9 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0311
-- Logloss: 1.0311
-- Accuracy: {'accuracy': 0.5626423690205011}
+- Loss: 0.9429
+- Logloss: 0.9429
+- Accuracy: {'accuracy': 0.6371511068334937}
 
 ## Model description
 
@@ -48,41 +48,22 @@ 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_ratio: 0.1
-- num_epochs: 30
+- num_epochs: 10
 
 ### Training results
 
-| Training Loss | Epoch   | Step | Validation Loss | Logloss | Accuracy                          |
-|:-------------:|:-------:|:----:|:---------------:|:-------:|:---------------------------------:|
-| 1.6135        | 0.9455  | 13   | 1.5881          | 1.5881  | {'accuracy': 0.22323462414578588} |
-| 1.5823        | 1.9636  | 27   | 1.5302          | 1.5302  | {'accuracy': 0.35990888382687924} |
-| 1.4988        | 2.9818  | 41   | 1.4480          | 1.4480  | {'accuracy': 0.4874715261958998}  |
-| 1.4303        | 4.0     | 55   | 1.3424          | 1.3424  | {'accuracy': 0.5034168564920274}  |
-| 1.3653        | 4.9455  | 68   | 1.2544          | 1.2544  | {'accuracy': 0.5239179954441914}  |
-| 1.2232        | 5.9636  | 82   | 1.1867          | 1.1867  | {'accuracy': 0.5239179954441914}  |
-| 1.1734        | 6.9818  | 96   | 1.1330          | 1.1330  | {'accuracy': 0.5466970387243736}  |
-| 1.0747        | 8.0     | 110  | 1.1197          | 1.1197  | {'accuracy': 0.5626423690205011}  |
-| 1.0405        | 8.9455  | 123  | 1.0871          | 1.0871  | {'accuracy': 0.5535307517084282}  |
-| 1.0313        | 9.9636  | 137  | 1.0900          | 1.0900  | {'accuracy': 0.5671981776765376}  |
-| 0.959         | 10.9818 | 151  | 1.0766          | 1.0766  | {'accuracy': 0.5603644646924829}  |
-| 0.9314        | 12.0    | 165  | 1.0608          | 1.0608  | {'accuracy': 0.5603644646924829}  |
-| 0.9102        | 12.9455 | 178  | 1.0388          | 1.0388  | {'accuracy': 0.5649202733485194}  |
-| 0.8437        | 13.9636 | 192  | 1.0332          | 1.0332  | {'accuracy': 0.5785876993166287}  |
-| 0.8234        | 14.9818 | 206  | 1.0302          | 1.0302  | {'accuracy': 0.5763097949886105}  |
-| 0.7883        | 16.0    | 220  | 1.0276          | 1.0276  | {'accuracy': 0.5649202733485194}  |
-| 0.7364        | 16.9455 | 233  | 1.0278          | 1.0278  | {'accuracy': 0.5649202733485194}  |
-| 0.7561        | 17.9636 | 247  | 1.0258          | 1.0258  | {'accuracy': 0.5649202733485194}  |
-| 0.7062        | 18.9818 | 261  | 1.0196          | 1.0196  | {'accuracy': 0.5694760820045558}  |
-| 0.6897        | 20.0    | 275  | 1.0308          | 1.0308  | {'accuracy': 0.5558086560364465}  |
-| 0.6511        | 20.9455 | 288  | 1.0247          | 1.0247  | {'accuracy': 0.5626423690205011}  |
-| 0.6338        | 21.9636 | 302  | 1.0310          | 1.0310  | {'accuracy': 0.5603644646924829}  |
-| 0.619         | 22.9818 | 316  | 1.0258          | 1.0258  | {'accuracy': 0.5671981776765376}  |
-| 0.6008        | 24.0    | 330  | 1.0299          | 1.0299  | {'accuracy': 0.5626423690205011}  |
-| 0.601         | 24.9455 | 343  | 1.0329          | 1.0329  | {'accuracy': 0.5671981776765376}  |
-| 0.595         | 25.9636 | 357  | 1.0277          | 1.0277  | {'accuracy': 0.5694760820045558}  |
-| 0.598         | 26.9818 | 371  | 1.0288          | 1.0288  | {'accuracy': 0.5671981776765376}  |
-| 0.5771        | 28.0    | 385  | 1.0311          | 1.0311  | {'accuracy': 0.5603644646924829}  |
-| 0.5829        | 28.3636 | 390  | 1.0311          | 1.0311  | {'accuracy': 0.5626423690205011}  |
+| Training Loss | Epoch  | Step | Validation Loss | Logloss | Accuracy                         |
+|:-------------:|:------:|:----:|:---------------:|:-------:|:--------------------------------:|
+| 1.4538        | 0.9846 | 32   | 1.3816          | 1.3816  | {'accuracy': 0.4475457170356112} |
+| 1.2257        | 2.0    | 65   | 1.1411          | 1.1411  | {'accuracy': 0.5668912415784408} |
+| 1.0432        | 2.9846 | 97   | 1.0302          | 1.0302  | {'accuracy': 0.6034648700673725} |
+| 1.0002        | 4.0    | 130  | 0.9979          | 0.9979  | {'accuracy': 0.615014436958614}  |
+| 0.9492        | 4.9846 | 162  | 0.9781          | 0.9781  | {'accuracy': 0.631376323387873}  |
+| 0.9302        | 6.0    | 195  | 0.9664          | 0.9664  | {'accuracy': 0.629451395572666}  |
+| 0.8805        | 6.9846 | 227  | 0.9515          | 0.9515  | {'accuracy': 0.6371511068334937} |
+| 0.852         | 8.0    | 260  | 0.9504          | 0.9504  | {'accuracy': 0.6256015399422522} |
+| 0.8352        | 8.9846 | 292  | 0.9468          | 0.9468  | {'accuracy': 0.6371511068334937} |
+| 0.8245        | 9.8462 | 320  | 0.9429          | 0.9429  | {'accuracy': 0.6371511068334937} |
 
 
 ### Framework versions