Model save

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 1.0
+      value: 0.6985040276179517
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,30 +32,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0000
-- Accuracy: 1.0
+- Loss: 0.7308
+- Accuracy: 0.6985
 
 ## Model description
 
-This model was created by importing the dataset of the photos of ECG image into Google Colab from kaggle here: https://www.kaggle.com/datasets/erhmrai/ecg-image-data/data . I then used the image classification tutorial here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb 
-
-obtaining the following notebook:
-
-https://colab.research.google.com/drive/1KC6twirtsc7N1kmlwY3IQKVUmSuK7zlh?usp=sharing
-
-The possible classified data are:
-<ul>
-<li>N: Normal beat</li>
-<li>S: Supraventricular premature beat</li>
-<li>V: Premature ventricular contraction</li>
-<li>F: Fusion of ventricular and normal beat</li>
-<li>Q: Unclassifiable beat</li>
-<li>M: myocardial infarction</li>
-</ul>
-
-### ECG example:
-
-![Screenshot](N1.png)
+More information needed
 
 ## Intended uses & limitations
 
@@ -85,12 +67,12 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.0361        | 1.0   | 697  | 0.0000          | 1.0      |
+| 0.7715        | 1.0   | 183  | 0.7308          | 0.6985   |
 
 
 ### Framework versions
 
-- Transformers 4.34.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.14.1
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

all_results.json

@@ -1,8 +1,8 @@
 {
     "epoch": 1.0,
-    "eval_accuracy": 1.0,
-    "eval_loss": 1.7099075932947017e-07,
-    "eval_runtime": 90.1655,
-    "eval_samples_per_second": 110.02,
-    "eval_steps_per_second": 3.438
+    "eval_accuracy": 0.6985040276179517,
+    "eval_loss": 0.7308106422424316,
+    "eval_runtime": 65.3069,
+    "eval_samples_per_second": 39.919,
+    "eval_steps_per_second": 1.256
 }

eval_results.json

@@ -1,8 +1,8 @@
 {
     "epoch": 1.0,
-    "eval_accuracy": 1.0,
-    "eval_loss": 1.7099075932947017e-07,
-    "eval_runtime": 90.1655,
-    "eval_samples_per_second": 110.02,
-    "eval_steps_per_second": 3.438
+    "eval_accuracy": 0.6985040276179517,
+    "eval_loss": 0.7308106422424316,
+    "eval_runtime": 65.3069,
+    "eval_samples_per_second": 39.919,
+    "eval_steps_per_second": 1.256
 }

runs/Jan15_05-27-24_754e893eb757/events.out.tfevents.1705297400.754e893eb757.393.1

@@ -0,0 +1,3 @@
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