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google-vit-base-patch16-224-Waste-O-I-classification

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- ---
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- license: apache-2.0
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- base_model: google/vit-base-patch16-224
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- tags:
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- - generated_from_trainer
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- datasets:
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- - imagefolder
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- metrics:
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- - accuracy
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- model-index:
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- - name: google-vit-base-patch16-224-Waste-O-I-classification
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- results:
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- - task:
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- name: Image Classification
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- type: image-classification
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- dataset:
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- name: imagefolder
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- type: imagefolder
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- config: default
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- split: train
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- args: default
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- metrics:
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- - name: Accuracy
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- type: accuracy
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- value: 0.956
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- ---
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-
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # google-vit-base-patch16-224-Waste-O-I-classification
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-
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- This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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- It achieves the following results on the evaluation set:
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- - Accuracy: 0.956
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- - Loss: 0.3036
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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- - train_batch_size: 8
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- - eval_batch_size: 8
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- - seed: 42
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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- - num_epochs: 4
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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- |:-------------:|:------:|:-----:|:--------:|:---------------:|
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- | 0.2168 | 0.1580 | 1000 | 0.9525 | 0.1303 |
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- | 0.196 | 0.3159 | 2000 | 0.941 | 0.1638 |
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- | 0.1993 | 0.4739 | 3000 | 0.9285 | 0.2206 |
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- | 0.1849 | 0.6318 | 4000 | 0.9225 | 0.2288 |
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- | 0.199 | 0.7898 | 5000 | 0.9105 | 0.3331 |
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- | 0.2171 | 0.9477 | 6000 | 0.944 | 0.1582 |
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- | 0.1209 | 1.1057 | 7000 | 0.9495 | 0.1887 |
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- | 0.114 | 1.2636 | 8000 | 0.932 | 0.1950 |
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- | 0.1268 | 1.4216 | 9000 | 0.9335 | 0.1965 |
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- | 0.1272 | 1.5795 | 10000 | 0.9165 | 0.3112 |
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- | 0.1003 | 1.7375 | 11000 | 0.9575 | 0.1353 |
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- | 0.0844 | 1.8954 | 12000 | 0.9345 | 0.2635 |
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- | 0.0757 | 2.0534 | 13000 | 0.952 | 0.1434 |
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- | 0.053 | 2.2113 | 14000 | 0.933 | 0.3203 |
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- | 0.0994 | 2.3693 | 15000 | 0.9405 | 0.2165 |
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- | 0.0248 | 2.5272 | 16000 | 0.951 | 0.2400 |
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- | 0.0842 | 2.6852 | 17000 | 0.906 | 0.4092 |
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- | 0.0733 | 2.8432 | 18000 | 0.9515 | 0.1937 |
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- | 0.0542 | 3.0011 | 19000 | 0.938 | 0.2911 |
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- | 0.0202 | 3.1591 | 20000 | 0.936 | 0.3648 |
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- | 0.0237 | 3.3170 | 21000 | 0.9355 | 0.3618 |
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- | 0.0294 | 3.4750 | 22000 | 0.9255 | 0.4209 |
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- | 0.0375 | 3.6329 | 23000 | 0.943 | 0.2840 |
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- | 0.0176 | 3.7909 | 24000 | 0.9525 | 0.2604 |
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- | 0.0252 | 3.9488 | 25000 | 0.9515 | 0.2500 |
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- | 0.0024 | 4.1068 | 26000 | 0.9545 | 0.2892 |
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- | 0.0119 | 4.2647 | 27000 | 0.956 | 0.3036 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.44.0
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- - Pytorch 2.4.0+cpu
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- - Datasets 2.20.0
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- - Tokenizers 0.19.1
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - MiguelCalderon/TGdataTrain
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+ - MiguelCalderon/TGdataTest
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: google-vit-base-patch16-224-Waste-O-I-classification
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.956
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+ language:
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+ - es
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+ - en
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+ pipeline_tag: image-classification
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+ library_name: transformers
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # google-vit-base-patch16-224-Waste-O-I-classification
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
40
+ It achieves the following results on the evaluation set:
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+ - Accuracy: 0.956
42
+ - Loss: 0.3036
43
+
44
+ ## Model description
45
+
46
+ More information needed
47
+
48
+ ## Intended uses & limitations
49
+
50
+ More information needed
51
+
52
+ ## Training and evaluation data
53
+
54
+ More information needed
55
+
56
+ ## Training procedure
57
+
58
+ ### Training hyperparameters
59
+
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+ The following hyperparameters were used during training:
61
+ - learning_rate: 0.0002
62
+ - train_batch_size: 8
63
+ - eval_batch_size: 8
64
+ - seed: 42
65
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 4
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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+ |:-------------:|:------:|:-----:|:--------:|:---------------:|
73
+ | 0.2168 | 0.1580 | 1000 | 0.9525 | 0.1303 |
74
+ | 0.196 | 0.3159 | 2000 | 0.941 | 0.1638 |
75
+ | 0.1993 | 0.4739 | 3000 | 0.9285 | 0.2206 |
76
+ | 0.1849 | 0.6318 | 4000 | 0.9225 | 0.2288 |
77
+ | 0.199 | 0.7898 | 5000 | 0.9105 | 0.3331 |
78
+ | 0.2171 | 0.9477 | 6000 | 0.944 | 0.1582 |
79
+ | 0.1209 | 1.1057 | 7000 | 0.9495 | 0.1887 |
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+ | 0.114 | 1.2636 | 8000 | 0.932 | 0.1950 |
81
+ | 0.1268 | 1.4216 | 9000 | 0.9335 | 0.1965 |
82
+ | 0.1272 | 1.5795 | 10000 | 0.9165 | 0.3112 |
83
+ | 0.1003 | 1.7375 | 11000 | 0.9575 | 0.1353 |
84
+ | 0.0844 | 1.8954 | 12000 | 0.9345 | 0.2635 |
85
+ | 0.0757 | 2.0534 | 13000 | 0.952 | 0.1434 |
86
+ | 0.053 | 2.2113 | 14000 | 0.933 | 0.3203 |
87
+ | 0.0994 | 2.3693 | 15000 | 0.9405 | 0.2165 |
88
+ | 0.0248 | 2.5272 | 16000 | 0.951 | 0.2400 |
89
+ | 0.0842 | 2.6852 | 17000 | 0.906 | 0.4092 |
90
+ | 0.0733 | 2.8432 | 18000 | 0.9515 | 0.1937 |
91
+ | 0.0542 | 3.0011 | 19000 | 0.938 | 0.2911 |
92
+ | 0.0202 | 3.1591 | 20000 | 0.936 | 0.3648 |
93
+ | 0.0237 | 3.3170 | 21000 | 0.9355 | 0.3618 |
94
+ | 0.0294 | 3.4750 | 22000 | 0.9255 | 0.4209 |
95
+ | 0.0375 | 3.6329 | 23000 | 0.943 | 0.2840 |
96
+ | 0.0176 | 3.7909 | 24000 | 0.9525 | 0.2604 |
97
+ | 0.0252 | 3.9488 | 25000 | 0.9515 | 0.2500 |
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+ | 0.0024 | 4.1068 | 26000 | 0.9545 | 0.2892 |
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+ | 0.0119 | 4.2647 | 27000 | 0.956 | 0.3036 |
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+
101
+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
105
+ - Pytorch 2.4.0+cpu
106
+ - Datasets 2.20.0
107
+ - Tokenizers 0.19.1