surajjoshi commited on
Commit
8d01c24
1 Parent(s): 84e6c5e

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +94 -0
README.md ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - imagefolder
7
+ metrics:
8
+ - accuracy
9
+ - f1
10
+ - recall
11
+ - precision
12
+ model-index:
13
+ - name: Brain_Tumor_Classification_using_swin_transformer
14
+ results:
15
+ - task:
16
+ name: Image Classification
17
+ type: image-classification
18
+ dataset:
19
+ name: imagefolder
20
+ type: imagefolder
21
+ config: default
22
+ split: train
23
+ args: default
24
+ metrics:
25
+ - name: Accuracy
26
+ type: accuracy
27
+ value: 0.9949179046129789
28
+ - name: F1
29
+ type: f1
30
+ value: 0.9949179046129789
31
+ - name: Recall
32
+ type: recall
33
+ value: 0.9949179046129789
34
+ - name: Precision
35
+ type: precision
36
+ value: 0.9949179046129789
37
+ ---
38
+
39
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
40
+ should probably proofread and complete it, then remove this comment. -->
41
+
42
+ # Brain_Tumor_Classification_using_swin_transformer
43
+
44
+ This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
45
+ It achieves the following results on the evaluation set:
46
+ - Loss: 0.0118
47
+ - Accuracy: 0.9949
48
+ - F1: 0.9949
49
+ - Recall: 0.9949
50
+ - Precision: 0.9949
51
+
52
+ ## Model description
53
+
54
+ More information needed
55
+
56
+ ## Intended uses & limitations
57
+
58
+ More information needed
59
+
60
+ ## Training and evaluation data
61
+
62
+ More information needed
63
+
64
+ ## Training procedure
65
+
66
+ ### Training hyperparameters
67
+
68
+ The following hyperparameters were used during training:
69
+ - learning_rate: 5e-05
70
+ - train_batch_size: 32
71
+ - eval_batch_size: 32
72
+ - seed: 42
73
+ - gradient_accumulation_steps: 4
74
+ - total_train_batch_size: 128
75
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
76
+ - lr_scheduler_type: linear
77
+ - lr_scheduler_warmup_ratio: 0.1
78
+ - num_epochs: 3
79
+
80
+ ### Training results
81
+
82
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
83
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
84
+ | 0.081 | 1.0 | 180 | 0.0557 | 0.9832 | 0.9832 | 0.9832 | 0.9832 |
85
+ | 0.0816 | 2.0 | 360 | 0.0187 | 0.9937 | 0.9937 | 0.9937 | 0.9937 |
86
+ | 0.0543 | 3.0 | 540 | 0.0118 | 0.9949 | 0.9949 | 0.9949 | 0.9949 |
87
+
88
+
89
+ ### Framework versions
90
+
91
+ - Transformers 4.23.1
92
+ - Pytorch 1.13.0
93
+ - Datasets 2.6.1
94
+ - Tokenizers 0.13.1