End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: mit
-base_model: microsoft/table-transformer-detection
+base_model: ShaneOD2611/table-transformer-detection_finetuned_CSGO
 tags:
 - generated_from_trainer
 model-index:
@@ -13,9 +13,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 # table-transformer-detection_finetuned_CSGO
 
-This model is a fine-tuned version of [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) on an unknown dataset.
+This model is a fine-tuned version of [ShaneOD2611/table-transformer-detection_finetuned_CSGO](https://huggingface.co/ShaneOD2611/table-transformer-detection_finetuned_CSGO) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.0076
+- Loss: 1.3249
 
 ## Model description
 
@@ -40,14 +40,23 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 1.9004        | 1.0   | 222  | 2.0076          |
+| 1.9861        | 1.0   | 222  | 2.0718          |
+| 1.6974        | 2.0   | 444  | 1.7959          |
+| 1.4636        | 3.0   | 666  | 1.5956          |
+| 1.4833        | 4.0   | 888  | 1.6272          |
+| 1.4682        | 5.0   | 1110 | 1.5899          |
+| 1.4169        | 6.0   | 1332 | 1.4784          |
+| 1.2369        | 7.0   | 1554 | 1.4201          |
+| 1.1632        | 8.0   | 1776 | 1.3718          |
+| 1.1439        | 9.0   | 1998 | 1.3511          |
+| 1.0676        | 10.0  | 2220 | 1.3249          |
 
 
 ### Framework versions

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "microsoft/table-transformer-detection",
+  "_name_or_path": "ShaneOD2611/table-transformer-detection_finetuned_CSGO",
   "activation_dropout": 0.0,
   "activation_function": "relu",
   "architectures": [

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