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---
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license: apache-2.0
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base_model: Xrenya/pvt-small-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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- f1
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- precision
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- recall
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model-index:
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- name: pvt-small-224-ConcreteClassifier-PVT
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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:
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accuracy: 0.17665369649805449
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- name: F1
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type: f1
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value:
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f1: 0.04289493575207861
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- name: Precision
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type: precision
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value:
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precision: 0.025236242356864926
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- name: Recall
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type: recall
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value:
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recall: 0.14285714285714285
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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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# pvt-small-224-ConcreteClassifier-PVT
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This model is a fine-tuned version of [Xrenya/pvt-small-224](https://huggingface.co/Xrenya/pvt-small-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9419
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- Accuracy: {'accuracy': 0.17665369649805449}
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- F1: {'f1': 0.04289493575207861}
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- Precision: {'precision': 0.025236242356864926}
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- Recall: {'recall': 0.14285714285714285}
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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: 0.001
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------:|:---------------------------:|:-----------------------------------:|:-------------------------------:|
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| 1.981 | 1.0 | 1927 | 1.9584 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.951 | 2.0 | 3854 | 1.9447 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9799 | 3.0 | 5781 | 1.9498 | {'accuracy': 0.13618677042801555} | {'f1': 0.03424657534246575} | {'precision': 0.019455252918287935} | {'recall': 0.14285714285714285} |
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| 1.9458 | 4.0 | 7708 | 1.9412 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9444 | 5.0 | 9635 | 1.9408 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9441 | 6.0 | 11562 | 1.9427 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9379 | 7.0 | 13489 | 1.9433 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9529 | 8.0 | 15416 | 1.9432 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9305 | 9.0 | 17343 | 1.9463 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.94 | 10.0 | 19270 | 1.9412 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.945 | 11.0 | 21197 | 1.9432 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9294 | 12.0 | 23124 | 1.9444 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9339 | 13.0 | 25051 | 1.9415 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.934 | 14.0 | 26978 | 1.9408 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9275 | 15.0 | 28905 | 1.9423 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9539 | 16.0 | 30832 | 1.9440 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9584 | 17.0 | 32759 | 1.9412 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9409 | 18.0 | 34686 | 1.9405 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9522 | 19.0 | 36613 | 1.9405 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9296 | 20.0 | 38540 | 1.9410 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9272 | 21.0 | 40467 | 1.9412 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9399 | 22.0 | 42394 | 1.9413 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9258 | 23.0 | 44321 | 1.9413 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9481 | 24.0 | 46248 | 1.9422 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.948 | 25.0 | 48175 | 1.9423 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.918 | 26.0 | 50102 | 1.9416 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.938 | 27.0 | 52029 | 1.9414 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9207 | 28.0 | 53956 | 1.9410 | {'accuracy': 0.1556420233463035} | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
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| 1.9472 | 29.0 | 55883 | 1.9404 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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| 1.9355 | 30.0 | 57810 | 1.9419 | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.1.0
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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