End of training

README.md

@@ -2,23 +2,38 @@
 license: apache-2.0
 base_model: facebook/convnextv2-tiny-1k-224
 tags:
-- generated_from_keras_callback
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
 model-index:
-- name: LaLegumbreArtificial/cv_model_DP_1
-  results: []
+- name: cv_model_DP_1
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9932885906040269
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
+<!-- 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. -->
 
-# LaLegumbreArtificial/cv_model_DP_1
+# cv_model_DP_1
 
-This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
+This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.3776
-- Validation Loss: 0.4951
-- Train Accuracy: 0.7315
-- Epoch: 4
+- Loss: 0.0204
+- Accuracy: 0.9933
 
 ## Model description
 
@@ -37,23 +52,31 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
-- training_precision: float32
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
 
 ### Training results
 
-| Train Loss | Validation Loss | Train Accuracy | Epoch |
-|:----------:|:---------------:|:--------------:|:-----:|
-| 0.5322     | 0.4468          | 0.8297         | 0     |
-| 0.4456     | 0.4137          | 0.8138         | 1     |
-| 0.4190     | 0.3926          | 0.9027         | 2     |
-| 0.3930     | 0.3833          | 0.8926         | 3     |
-| 0.3776     | 0.4951          | 0.7315         | 4     |
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.0547        | 0.9954 | 162  | 0.0647          | 0.9790   |
+| 0.0356        | 1.9969 | 325  | 0.0438          | 0.9841   |
+| 0.0187        | 2.9985 | 488  | 0.0260          | 0.9908   |
+| 0.0049        | 4.0    | 651  | 0.0360          | 0.9874   |
+| 0.0165        | 4.9770 | 810  | 0.0204          | 0.9933   |
 
 
 ### Framework versions
 
 - Transformers 4.41.2
-- TensorFlow 2.15.0
+- Pytorch 2.1.2
 - Datasets 2.19.2
 - Tokenizers 0.19.1