Hugging Face's logo

DunnBC22
/
dit-base-Business_Documents_Classified_v2

Image Classification
Transformers
PyTorch
TensorBoard
English
beit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
2
DunnBC22 commited on
Commit
a6eb8b5
•
1 Parent(s): 5a859bc

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +102 -0
README.md ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - generated_from_trainer
4
+ datasets:
5
+ - imagefolder
6
+ metrics:
7
+ - accuracy
8
+ model-index:
9
+ - name: dit-base-Business_Documents_Classified_v2
10
+ results:
11
+ - task:
12
+ name: Image Classification
13
+ type: image-classification
14
+ dataset:
15
+ name: imagefolder
16
+ type: imagefolder
17
+ config: data
18
+ split: train
19
+ args: data
20
+ metrics:
21
+ - name: Accuracy
22
+ type: accuracy
23
+ value: 0.826
24
+ ---
25
+
26
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
27
+ should probably proofread and complete it, then remove this comment. -->
28
+
29
+ # dit-base-Business_Documents_Classified_v2
30
+
31
+ This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
32
+ It achieves the following results on the evaluation set:
33
+ - Loss: 0.6715
34
+ - Accuracy: 0.826
35
+ - Weighted f1: 0.8272
36
+ - Micro f1: 0.826
37
+ - Macro f1: 0.8242
38
+ - Weighted recall: 0.826
39
+ - Micro recall: 0.826
40
+ - Macro recall: 0.8237
41
+ - Weighted precision: 0.8327
42
+ - Micro precision: 0.826
43
+ - Macro precision: 0.8293
44
+
45
+ ## Model description
46
+
47
+ More information needed
48
+
49
+ ## Intended uses & limitations
50
+
51
+ More information needed
52
+
53
+ ## Training and evaluation data
54
+
55
+ More information needed
56
+
57
+ ## Training procedure
58
+
59
+ ### Training hyperparameters
60
+
61
+ The following hyperparameters were used during training:
62
+ - learning_rate: 5e-05
63
+ - train_batch_size: 32
64
+ - eval_batch_size: 32
65
+ - seed: 42
66
+ - gradient_accumulation_steps: 4
67
+ - total_train_batch_size: 128
68
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
69
+ - lr_scheduler_type: linear
70
+ - lr_scheduler_warmup_ratio: 0.1
71
+ - num_epochs: 18
72
+
73
+ ### Training results
74
+
75
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
76
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
77
+ | 2.7266 | 0.99 | 31 | 2.4738 | 0.208 | 0.1811 | 0.208 | 0.1827 | 0.208 | 0.208 | 0.2101 | 0.2143 | 0.208 | 0.2246 |
78
+ | 2.171 | 1.98 | 62 | 1.8510 | 0.423 | 0.3936 | 0.4230 | 0.3925 | 0.423 | 0.423 | 0.4243 | 0.4503 | 0.423 | 0.4446 |
79
+ | 1.6525 | 2.98 | 93 | 1.2633 | 0.61 | 0.5884 | 0.61 | 0.5855 | 0.61 | 0.61 | 0.6124 | 0.6377 | 0.61 | 0.6283 |
80
+ | 1.346 | 4.0 | 125 | 1.0259 | 0.706 | 0.7023 | 0.706 | 0.6992 | 0.706 | 0.706 | 0.7058 | 0.7095 | 0.706 | 0.7034 |
81
+ | 1.253 | 4.99 | 156 | 0.9180 | 0.729 | 0.7277 | 0.729 | 0.7239 | 0.729 | 0.729 | 0.7291 | 0.7340 | 0.729 | 0.7261 |
82
+ | 1.0975 | 5.98 | 187 | 0.8859 | 0.747 | 0.7480 | 0.747 | 0.7437 | 0.747 | 0.747 | 0.7472 | 0.7609 | 0.747 | 0.7526 |
83
+ | 1.1122 | 6.98 | 218 | 0.8270 | 0.76 | 0.7606 | 0.76 | 0.7578 | 0.76 | 0.76 | 0.7594 | 0.7772 | 0.76 | 0.7727 |
84
+ | 1.0365 | 8.0 | 250 | 0.7806 | 0.775 | 0.7759 | 0.775 | 0.7730 | 0.775 | 0.775 | 0.7735 | 0.7957 | 0.775 | 0.7920 |
85
+ | 1.004 | 8.99 | 281 | 0.7472 | 0.796 | 0.7977 | 0.796 | 0.7957 | 0.796 | 0.796 | 0.7956 | 0.8193 | 0.796 | 0.8151 |
86
+ | 0.9278 | 9.98 | 312 | 0.7296 | 0.795 | 0.7974 | 0.795 | 0.7957 | 0.795 | 0.795 | 0.7953 | 0.8157 | 0.795 | 0.8115 |
87
+ | 0.8767 | 10.98 | 343 | 0.7257 | 0.809 | 0.8101 | 0.809 | 0.8078 | 0.809 | 0.809 | 0.8091 | 0.8182 | 0.809 | 0.8136 |
88
+ | 0.8656 | 12.0 | 375 | 0.6875 | 0.814 | 0.8137 | 0.8140 | 0.8106 | 0.814 | 0.814 | 0.8122 | 0.8207 | 0.814 | 0.8164 |
89
+ | 0.7905 | 12.99 | 406 | 0.7060 | 0.808 | 0.8093 | 0.808 | 0.8071 | 0.808 | 0.808 | 0.8068 | 0.8182 | 0.808 | 0.8145 |
90
+ | 0.8804 | 13.98 | 437 | 0.6849 | 0.82 | 0.8214 | 0.82 | 0.8183 | 0.82 | 0.82 | 0.8183 | 0.8260 | 0.82 | 0.8215 |
91
+ | 0.8265 | 14.98 | 468 | 0.6821 | 0.816 | 0.8171 | 0.816 | 0.8143 | 0.816 | 0.816 | 0.8142 | 0.8242 | 0.816 | 0.8206 |
92
+ | 0.7929 | 16.0 | 500 | 0.6877 | 0.818 | 0.8184 | 0.818 | 0.8152 | 0.818 | 0.818 | 0.8167 | 0.8240 | 0.818 | 0.8186 |
93
+ | 0.7993 | 16.99 | 531 | 0.6718 | 0.825 | 0.8259 | 0.825 | 0.8234 | 0.825 | 0.825 | 0.8227 | 0.8306 | 0.825 | 0.8282 |
94
+ | 0.7954 | 17.86 | 558 | 0.6715 | 0.826 | 0.8272 | 0.826 | 0.8242 | 0.826 | 0.826 | 0.8237 | 0.8327 | 0.826 | 0.8293 |
95
+
96
+
97
+ ### Framework versions
98
+
99
+ - Transformers 4.28.1
100
+ - Pytorch 2.0.0+cu118
101
+ - Datasets 2.11.0
102
+ - Tokenizers 0.13.3