update model card README.md

README.md

@@ -1,7 +1,8 @@
 ---
-license: apache-2.0
-base_model: microsoft/resnet-50
+license: other
+base_model: google/mobilenet_v2_0.75_160
 tags:
+- image-classification
 - generated_from_trainer
 datasets:
 - imagefolder
@@ -22,7 +23,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 1.0
+      value: 0.9988452655889145
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +31,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # day-night
 
-This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
+This model is a fine-tuned version of [google/mobilenet_v2_0.75_160](https://huggingface.co/google/mobilenet_v2_0.75_160) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0612
-- Accuracy: 1.0
+- Loss: 0.0030
+- Accuracy: 0.9988
 
 ## Model description
 
@@ -53,7 +54,7 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0001
-- train_batch_size: 8
+- train_batch_size: 10
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@@ -64,11 +65,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.585         | 1.0   | 100  | 0.4259          | 0.95     |
-| 0.3643        | 2.0   | 200  | 0.1822          | 0.99     |
-| 0.2618        | 3.0   | 300  | 0.1002          | 0.99     |
-| 0.1678        | 4.0   | 400  | 0.0644          | 1.0      |
-| 0.1619        | 5.0   | 500  | 0.0612          | 1.0      |
+| 0.1021        | 0.6   | 200  | 0.0679          | 0.9734   |
+| 0.0199        | 1.19  | 400  | 0.0184          | 0.9919   |
+| 0.0723        | 1.79  | 600  | 0.6625          | 0.7852   |
+| 0.0247        | 2.38  | 800  | 0.0030          | 0.9988   |
+| 0.0273        | 2.98  | 1000 | 0.0254          | 0.9885   |
+| 0.012         | 3.57  | 1200 | 0.0177          | 0.9965   |
+| 0.0142        | 4.17  | 1400 | 0.0282          | 0.9908   |
+| 0.009         | 4.76  | 1600 | 0.0189          | 0.9977   |
 
 
 ### Framework versions