End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.48717948717948717
+      value: 0.9944991748762314
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [google/efficientnet-b2](https://huggingface.co/google/efficientnet-b2) on the image_folder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6971
-- Accuracy: 0.4872
+- Loss: 0.0158
+- Accuracy: 0.9945
 
 ## Model description
 
@@ -65,6 +65,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.6956        | 0.26  | 100  | 0.6719          | 0.5956   |
+| 0.6424        | 0.51  | 200  | 0.5992          | 0.7267   |
+| 0.5465        | 0.77  | 300  | 0.4793          | 0.7968   |
+| 0.4107        | 1.02  | 400  | 0.3361          | 0.8649   |
+| 0.2488        | 1.28  | 500  | 0.1690          | 0.9398   |
+| 0.1441        | 1.54  | 600  | 0.0882          | 0.9688   |
+| 0.0945        | 1.79  | 700  | 0.0595          | 0.9809   |
+| 0.0713        | 2.05  | 800  | 0.0467          | 0.9835   |
+| 0.0472        | 2.3   | 900  | 0.0320          | 0.9895   |
+| 0.0411        | 2.56  | 1000 | 0.0260          | 0.9917   |
+| 0.0302        | 2.82  | 1100 | 0.0241          | 0.9918   |
+| 0.0259        | 3.07  | 1200 | 0.0192          | 0.9934   |
+| 0.0197        | 3.33  | 1300 | 0.0157          | 0.9953   |
+| 0.018         | 3.58  | 1400 | 0.0146          | 0.9950   |
+| 0.0164        | 3.84  | 1500 | 0.0158          | 0.9945   |
 
 
 ### Framework versions

all_results.json

@@ -1,8 +1,8 @@
 {
-    "epoch": 3.82,
-    "eval_accuracy": 0.48717948717948717,
-    "eval_loss": 0.6970879435539246,
-    "eval_runtime": 6.463,
-    "eval_samples_per_second": 66.378,
-    "eval_steps_per_second": 2.166
+    "epoch": 3.99,
+    "eval_accuracy": 0.9944991748762314,
+    "eval_loss": 0.015824124217033386,
+    "eval_runtime": 115.2936,
+    "eval_samples_per_second": 173.444,
+    "eval_steps_per_second": 5.421
 }

eval_results.json

@@ -1,8 +1,8 @@
 {
-    "epoch": 3.82,
-    "eval_accuracy": 0.48717948717948717,
-    "eval_loss": 0.6970879435539246,
-    "eval_runtime": 6.463,
-    "eval_samples_per_second": 66.378,
-    "eval_steps_per_second": 2.166
+    "epoch": 3.99,
+    "eval_accuracy": 0.9944991748762314,
+    "eval_loss": 0.015824124217033386,
+    "eval_runtime": 115.2936,
+    "eval_samples_per_second": 173.444,
+    "eval_steps_per_second": 5.421
 }