Hugging Face's logo

AsiniJayakody
/
cifar_fine_tuning

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
AsiniJayakody commited on
Commit
9481b6d
•
1 Parent(s): 08d90a1

Model save

Browse files
Files changed (2) hide show
  1. README.md +199 -0
  2. model.safetensors +1 -1
README.md ADDED
@@ -0,0 +1,199 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: google/vit-base-patch16-224-in21k
4
+ tags:
5
+ - generated_from_trainer
6
+ datasets:
7
+ - cifar10
8
+ metrics:
9
+ - accuracy
10
+ model-index:
11
+ - name: cifar_fine_tuning
12
+ results:
13
+ - task:
14
+ name: Image Classification
15
+ type: image-classification
16
+ dataset:
17
+ name: cifar10
18
+ type: cifar10
19
+ config: plain_text
20
+ split: test
21
+ args: plain_text
22
+ metrics:
23
+ - name: Accuracy
24
+ type: accuracy
25
+ value: 0.9784
26
+ ---
27
+
28
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
29
+ should probably proofread and complete it, then remove this comment. -->
30
+
31
+ # cifar_fine_tuning
32
+
33
+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
34
+ It achieves the following results on the evaluation set:
35
+ - Loss: 0.1187
36
+ - Accuracy: 0.9784
37
+
38
+ ## Model description
39
+
40
+ More information needed
41
+
42
+ ## Intended uses & limitations
43
+
44
+ More information needed
45
+
46
+ ## Training and evaluation data
47
+
48
+ More information needed
49
+
50
+ ## Training procedure
51
+
52
+ ### Training hyperparameters
53
+
54
+ The following hyperparameters were used during training:
55
+ - learning_rate: 0.0002
56
+ - train_batch_size: 16
57
+ - eval_batch_size: 8
58
+ - seed: 42
59
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
60
+ - lr_scheduler_type: linear
61
+ - num_epochs: 4
62
+
63
+ ### Training results
64
+
65
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
66
+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
67
+ | 0.49 | 0.03 | 100 | 0.4099 | 0.9073 |
68
+ | 0.3651 | 0.06 | 200 | 0.2860 | 0.9267 |
69
+ | 0.3671 | 0.1 | 300 | 0.4049 | 0.8832 |
70
+ | 0.3793 | 0.13 | 400 | 0.3363 | 0.9 |
71
+ | 0.4412 | 0.16 | 500 | 0.3392 | 0.8998 |
72
+ | 0.2496 | 0.19 | 600 | 0.3285 | 0.8987 |
73
+ | 0.4256 | 0.22 | 700 | 0.3622 | 0.8919 |
74
+ | 0.4017 | 0.26 | 800 | 0.3954 | 0.8782 |
75
+ | 0.3091 | 0.29 | 900 | 0.2375 | 0.9269 |
76
+ | 0.17 | 0.32 | 1000 | 0.2150 | 0.9345 |
77
+ | 0.2855 | 0.35 | 1100 | 0.2194 | 0.9314 |
78
+ | 0.2582 | 0.38 | 1200 | 0.2473 | 0.9242 |
79
+ | 0.3127 | 0.42 | 1300 | 0.2789 | 0.9169 |
80
+ | 0.3907 | 0.45 | 1400 | 0.3239 | 0.9046 |
81
+ | 0.186 | 0.48 | 1500 | 0.2316 | 0.9315 |
82
+ | 0.2105 | 0.51 | 1600 | 0.2121 | 0.9325 |
83
+ | 0.2539 | 0.54 | 1700 | 0.2014 | 0.9376 |
84
+ | 0.3119 | 0.58 | 1800 | 0.2543 | 0.9218 |
85
+ | 0.2136 | 0.61 | 1900 | 0.2670 | 0.9183 |
86
+ | 0.2067 | 0.64 | 2000 | 0.2062 | 0.9401 |
87
+ | 0.242 | 0.67 | 2100 | 0.2852 | 0.9121 |
88
+ | 0.1743 | 0.7 | 2200 | 0.1895 | 0.9414 |
89
+ | 0.2458 | 0.74 | 2300 | 0.2358 | 0.9288 |
90
+ | 0.131 | 0.77 | 2400 | 0.2408 | 0.9221 |
91
+ | 0.4146 | 0.8 | 2500 | 0.2134 | 0.9344 |
92
+ | 0.3165 | 0.83 | 2600 | 0.2531 | 0.9248 |
93
+ | 0.2364 | 0.86 | 2700 | 0.1996 | 0.9377 |
94
+ | 0.2476 | 0.9 | 2800 | 0.1971 | 0.9393 |
95
+ | 0.298 | 0.93 | 2900 | 0.2114 | 0.9333 |
96
+ | 0.181 | 0.96 | 3000 | 0.1894 | 0.942 |
97
+ | 0.1499 | 0.99 | 3100 | 0.1819 | 0.9431 |
98
+ | 0.1636 | 1.02 | 3200 | 0.2383 | 0.9271 |
99
+ | 0.1163 | 1.06 | 3300 | 0.1888 | 0.9472 |
100
+ | 0.1179 | 1.09 | 3400 | 0.1788 | 0.9491 |
101
+ | 0.1505 | 1.12 | 3500 | 0.2084 | 0.9407 |
102
+ | 0.1135 | 1.15 | 3600 | 0.2637 | 0.9351 |
103
+ | 0.0996 | 1.18 | 3700 | 0.2281 | 0.9399 |
104
+ | 0.1469 | 1.22 | 3800 | 0.1789 | 0.9485 |
105
+ | 0.0902 | 1.25 | 3900 | 0.1599 | 0.9524 |
106
+ | 0.0456 | 1.28 | 4000 | 0.1803 | 0.9493 |
107
+ | 0.1423 | 1.31 | 4100 | 0.1510 | 0.9562 |
108
+ | 0.1269 | 1.34 | 4200 | 0.1549 | 0.9579 |
109
+ | 0.0713 | 1.38 | 4300 | 0.1833 | 0.9495 |
110
+ | 0.0731 | 1.41 | 4400 | 0.1747 | 0.9511 |
111
+ | 0.0488 | 1.44 | 4500 | 0.1530 | 0.9591 |
112
+ | 0.0538 | 1.47 | 4600 | 0.1870 | 0.9522 |
113
+ | 0.0972 | 1.5 | 4700 | 0.1547 | 0.9562 |
114
+ | 0.1294 | 1.54 | 4800 | 0.1846 | 0.9486 |
115
+ | 0.1035 | 1.57 | 4900 | 0.1609 | 0.9562 |
116
+ | 0.1564 | 1.6 | 5000 | 0.1877 | 0.9521 |
117
+ | 0.1143 | 1.63 | 5100 | 0.1606 | 0.9559 |
118
+ | 0.1239 | 1.66 | 5200 | 0.1457 | 0.9587 |
119
+ | 0.107 | 1.7 | 5300 | 0.1815 | 0.9526 |
120
+ | 0.0515 | 1.73 | 5400 | 0.1594 | 0.9568 |
121
+ | 0.0508 | 1.76 | 5500 | 0.1584 | 0.9579 |
122
+ | 0.0308 | 1.79 | 5600 | 0.1640 | 0.9543 |
123
+ | 0.0934 | 1.82 | 5700 | 0.1558 | 0.9535 |
124
+ | 0.0372 | 1.86 | 5800 | 0.1792 | 0.9481 |
125
+ | 0.063 | 1.89 | 5900 | 0.1453 | 0.9616 |
126
+ | 0.1284 | 1.92 | 6000 | 0.1719 | 0.9534 |
127
+ | 0.0987 | 1.95 | 6100 | 0.1321 | 0.9649 |
128
+ | 0.0736 | 1.98 | 6200 | 0.1754 | 0.9505 |
129
+ | 0.097 | 2.02 | 6300 | 0.1608 | 0.9565 |
130
+ | 0.0062 | 2.05 | 6400 | 0.1687 | 0.9576 |
131
+ | 0.0748 | 2.08 | 6500 | 0.2191 | 0.9488 |
132
+ | 0.0491 | 2.11 | 6600 | 0.1442 | 0.9644 |
133
+ | 0.0467 | 2.14 | 6700 | 0.1525 | 0.9636 |
134
+ | 0.042 | 2.18 | 6800 | 0.1440 | 0.9643 |
135
+ | 0.0249 | 2.21 | 6900 | 0.1526 | 0.9627 |
136
+ | 0.0887 | 2.24 | 7000 | 0.1858 | 0.9587 |
137
+ | 0.0438 | 2.27 | 7100 | 0.1485 | 0.9644 |
138
+ | 0.0434 | 2.3 | 7200 | 0.1640 | 0.9623 |
139
+ | 0.0216 | 2.34 | 7300 | 0.1685 | 0.9621 |
140
+ | 0.0496 | 2.37 | 7400 | 0.1612 | 0.9615 |
141
+ | 0.0512 | 2.4 | 7500 | 0.1554 | 0.9635 |
142
+ | 0.0173 | 2.43 | 7600 | 0.1424 | 0.9667 |
143
+ | 0.1097 | 2.46 | 7700 | 0.1691 | 0.9603 |
144
+ | 0.0072 | 2.5 | 7800 | 0.1693 | 0.9588 |
145
+ | 0.0417 | 2.53 | 7900 | 0.1669 | 0.9599 |
146
+ | 0.0624 | 2.56 | 8000 | 0.1409 | 0.9675 |
147
+ | 0.0513 | 2.59 | 8100 | 0.1401 | 0.9663 |
148
+ | 0.0083 | 2.62 | 8200 | 0.1340 | 0.9679 |
149
+ | 0.0144 | 2.66 | 8300 | 0.1378 | 0.9671 |
150
+ | 0.0958 | 2.69 | 8400 | 0.1385 | 0.9667 |
151
+ | 0.011 | 2.72 | 8500 | 0.1265 | 0.9689 |
152
+ | 0.0022 | 2.75 | 8600 | 0.1268 | 0.9671 |
153
+ | 0.0814 | 2.78 | 8700 | 0.1291 | 0.9695 |
154
+ | 0.0092 | 2.82 | 8800 | 0.1216 | 0.9714 |
155
+ | 0.0178 | 2.85 | 8900 | 0.1156 | 0.972 |
156
+ | 0.0082 | 2.88 | 9000 | 0.1070 | 0.9745 |
157
+ | 0.0325 | 2.91 | 9100 | 0.1110 | 0.9744 |
158
+ | 0.0197 | 2.94 | 9200 | 0.1244 | 0.972 |
159
+ | 0.0291 | 2.98 | 9300 | 0.1303 | 0.9719 |
160
+ | 0.0012 | 3.01 | 9400 | 0.1283 | 0.9714 |
161
+ | 0.0089 | 3.04 | 9500 | 0.1221 | 0.974 |
162
+ | 0.0115 | 3.07 | 9600 | 0.1241 | 0.9737 |
163
+ | 0.0015 | 3.1 | 9700 | 0.1335 | 0.971 |
164
+ | 0.0018 | 3.14 | 9800 | 0.1315 | 0.9716 |
165
+ | 0.0004 | 3.17 | 9900 | 0.1127 | 0.9739 |
166
+ | 0.0128 | 3.2 | 10000 | 0.1168 | 0.976 |
167
+ | 0.0194 | 3.23 | 10100 | 0.1193 | 0.9749 |
168
+ | 0.0003 | 3.26 | 10200 | 0.1145 | 0.9759 |
169
+ | 0.0056 | 3.3 | 10300 | 0.1231 | 0.9734 |
170
+ | 0.0089 | 3.33 | 10400 | 0.1392 | 0.9723 |
171
+ | 0.0032 | 3.36 | 10500 | 0.1274 | 0.9728 |
172
+ | 0.0003 | 3.39 | 10600 | 0.1236 | 0.9748 |
173
+ | 0.0002 | 3.42 | 10700 | 0.1299 | 0.9737 |
174
+ | 0.0055 | 3.46 | 10800 | 0.1307 | 0.9735 |
175
+ | 0.0008 | 3.49 | 10900 | 0.1348 | 0.9731 |
176
+ | 0.0003 | 3.52 | 11000 | 0.1345 | 0.973 |
177
+ | 0.0288 | 3.55 | 11100 | 0.1238 | 0.9759 |
178
+ | 0.0217 | 3.58 | 11200 | 0.1233 | 0.9754 |
179
+ | 0.0012 | 3.62 | 11300 | 0.1203 | 0.9768 |
180
+ | 0.0008 | 3.65 | 11400 | 0.1165 | 0.9768 |
181
+ | 0.0003 | 3.68 | 11500 | 0.1199 | 0.9776 |
182
+ | 0.0028 | 3.71 | 11600 | 0.1258 | 0.9764 |
183
+ | 0.0014 | 3.74 | 11700 | 0.1217 | 0.9766 |
184
+ | 0.0225 | 3.78 | 11800 | 0.1242 | 0.9763 |
185
+ | 0.0002 | 3.81 | 11900 | 0.1214 | 0.9778 |
186
+ | 0.0017 | 3.84 | 12000 | 0.1213 | 0.9775 |
187
+ | 0.0002 | 3.87 | 12100 | 0.1214 | 0.9778 |
188
+ | 0.001 | 3.9 | 12200 | 0.1207 | 0.9773 |
189
+ | 0.0008 | 3.94 | 12300 | 0.1190 | 0.9782 |
190
+ | 0.0002 | 3.97 | 12400 | 0.1187 | 0.9784 |
191
+ | 0.0003 | 4.0 | 12500 | 0.1187 | 0.9784 |
192
+
193
+
194
+ ### Framework versions
195
+
196
+ - Transformers 4.37.0
197
+ - Pytorch 2.1.2
198
+ - Datasets 2.1.0
199
+ - Tokenizers 0.15.1
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:3206dd63d6cd418fa9b29affc23cbf7b0b8a22af1c87ff44fec52d1d925d38d8
3
  size 343248584
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cfea5565fe0428ffd1d855f9e6c2d237eefff9d54941be732c1d489a78594123
3
  size 343248584