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