update model card README.md

README.md

@@ -1,7 +1,8 @@
 ---
-license: other
-base_model: google/mobilenet_v2_0.75_160
+license: apache-2.0
+base_model: microsoft/resnet-50
 tags:
+- image-classification
 - generated_from_trainer
 datasets:
 - imagefolder
@@ -22,7 +23,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9866666666666667
+      value: 0.9382716049382716
 ---
 
 <!-- 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. -->
 
 # camera-type
 
-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.
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0299
-- Accuracy: 0.9867
+- Loss: 0.1654
+- Accuracy: 0.9383
 
 ## 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
@@ -64,9 +65,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.1286        | 1.33  | 200  | 0.0727          | 0.98     |
-| 0.036         | 2.67  | 400  | 0.2050          | 0.9333   |
-| 0.0341        | 4.0   | 600  | 0.0299          | 0.9867   |
+| 0.4597        | 0.5   | 200  | 0.2801          | 0.9242   |
+| 0.1375        | 0.99  | 400  | 0.1654          | 0.9383   |
+| 0.0795        | 1.49  | 600  | 0.1904          | 0.9383   |
+| 0.0686        | 1.98  | 800  | 0.1810          | 0.9453   |
+| 0.026         | 2.48  | 1000 | 0.2216          | 0.9400   |
+| 0.0495        | 2.97  | 1200 | 0.2096          | 0.9453   |
+| 0.0487        | 3.47  | 1400 | 0.2174          | 0.9436   |
+| 0.0268        | 3.96  | 1600 | 0.2304          | 0.9453   |
+| 0.0254        | 4.46  | 1800 | 0.2574          | 0.9400   |
+| 0.0186        | 4.95  | 2000 | 0.3212          | 0.9383   |
 
 
 ### Framework versions