Model save

README.md

@@ -1,14 +1,8 @@
 ---
 library_name: transformers
-license: apache-2.0
-base_model: facebook/dinov2-small
+base_model: nguyenkhoa/dinov2_Liveness_detection_v2.1.4
 tags:
 - generated_from_trainer
-metrics:
-- accuracy
-- f1
-- recall
-- precision
 model-index:
 - name: dinov2_Liveness_detection_v2.2.1
   results: []
@@ -17,16 +11,10 @@ model-index:
 <!-- 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. -->
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/nguyenkhoaht002/liveness_detection/runs/wo6b0psl)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/nguyenkhoaht002/liveness_detection/runs/zdrli5b6)
 # dinov2_Liveness_detection_v2.2.1
 
-This model is a fine-tuned version of [facebook/dinov2-small](https://huggingface.co/facebook/dinov2-small) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0671
-- Accuracy: 0.9869
-- F1: 0.9868
-- Recall: 0.9869
-- Precision: 0.9870
+This model is a fine-tuned version of [nguyenkhoa/dinov2_Liveness_detection_v2.1.4](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.1.4) on an unknown dataset.
 
 ## Model description
 
@@ -46,39 +34,17 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 512
+- train_batch_size: 768
 - eval_batch_size: 8
 - seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- optimizer: Use 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: 5
 - mixed_precision_training: Native AMP
 
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
-|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
-| 0.183         | 0.8153 | 128  | 0.2473          | 0.9016   | 0.9039 | 0.9016 | 0.9123    |
-| 0.1022        | 1.6306 | 256  | 0.0750          | 0.9729   | 0.9727 | 0.9729 | 0.9737    |
-| 0.0432        | 2.4459 | 384  | 0.0575          | 0.9820   | 0.9820 | 0.9820 | 0.9823    |
-| 0.0247        | 3.2611 | 512  | 0.0507          | 0.9832   | 0.9832 | 0.9832 | 0.9833    |
-| 0.0115        | 4.0764 | 640  | 0.0536          | 0.9865   | 0.9864 | 0.9865 | 0.9866    |
-| 0.002         | 4.8917 | 768  | 0.0671          | 0.9869   | 0.9868 | 0.9869 | 0.9870    |
-
-### Evaluate results
-
-- APCER: 0.2490
-- BPCER: 0.0676
-- ACER: 0.1583
-
-- Accuracy: 0.81
-- F1: 0.84
-- Recall: 0.93
-- Precision: 0.62
-
 ### Framework versions
 
-- Transformers 4.44.2
-- Pytorch 2.4.1+cu121
+- Transformers 4.47.0
+- Pytorch 2.5.1+cu121
 - Datasets 3.2.0
-- Tokenizers 0.19.1
+- Tokenizers 0.21.0

runs/Jan22_04-19-16_ab507d0761b0/events.out.tfevents.1737519560.ab507d0761b0.18.0

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