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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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<!-- 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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# google-vit-base-patch16-224-Waste-O-I-classification |
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This model is a fine-tuned version performed by [Miguel Calderon](https://huggingface.co./MiguelCalderon) 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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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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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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### Framework versions |
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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 |
