Model save
Browse files- README.md +113 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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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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- webdataset
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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: frost-vision-v2-google_vit-base-patch16-224-v2024-11-11
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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: webdataset
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type: webdataset
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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: 0.9411971830985916
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- name: F1
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type: f1
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value: 0.8474885844748858
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- name: Precision
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type: precision
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value: 0.836036036036036
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- name: Recall
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type: recall
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value: 0.8592592592592593
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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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# frost-vision-v2-google_vit-base-patch16-224-v2024-11-11
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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 webdataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1712
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- Accuracy: 0.9412
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- F1: 0.8475
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- Precision: 0.8360
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- Recall: 0.8593
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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: 5e-05
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- train_batch_size: 16
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 30
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- mixed_precision_training: Native AMP
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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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| 0.3127 | 1.4085 | 100 | 0.2932 | 0.8940 | 0.6725 | 0.8153 | 0.5722 |
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| 0.193 | 2.8169 | 200 | 0.2136 | 0.9190 | 0.7834 | 0.7969 | 0.7704 |
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| 0.1503 | 4.2254 | 300 | 0.1815 | 0.9278 | 0.8100 | 0.8108 | 0.8093 |
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| 0.1313 | 5.6338 | 400 | 0.1623 | 0.9327 | 0.8183 | 0.8415 | 0.7963 |
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| 0.1166 | 7.0423 | 500 | 0.1658 | 0.9320 | 0.8224 | 0.8172 | 0.8278 |
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| 0.093 | 8.4507 | 600 | 0.1606 | 0.9384 | 0.8405 | 0.8276 | 0.8537 |
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| 0.0931 | 9.8592 | 700 | 0.1625 | 0.9366 | 0.8370 | 0.8191 | 0.8556 |
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| 0.0733 | 11.2676 | 800 | 0.1714 | 0.9356 | 0.8310 | 0.8287 | 0.8333 |
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| 0.0693 | 12.6761 | 900 | 0.1568 | 0.9398 | 0.8403 | 0.8475 | 0.8333 |
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| 0.0615 | 14.0845 | 1000 | 0.1666 | 0.9342 | 0.8270 | 0.8262 | 0.8278 |
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| 0.0562 | 15.4930 | 1100 | 0.1636 | 0.9394 | 0.8404 | 0.8420 | 0.8389 |
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| 0.0507 | 16.9014 | 1200 | 0.1613 | 0.9401 | 0.8435 | 0.8388 | 0.8481 |
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| 0.0552 | 18.3099 | 1300 | 0.1590 | 0.9412 | 0.8455 | 0.8447 | 0.8463 |
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| 0.0439 | 19.7183 | 1400 | 0.1704 | 0.9394 | 0.8425 | 0.8333 | 0.8519 |
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| 0.0367 | 21.1268 | 1500 | 0.1702 | 0.9426 | 0.8484 | 0.8523 | 0.8444 |
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| 0.0424 | 22.5352 | 1600 | 0.1685 | 0.9394 | 0.8419 | 0.8358 | 0.8481 |
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| 0.0306 | 23.9437 | 1700 | 0.1771 | 0.9380 | 0.8397 | 0.8262 | 0.8537 |
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| 0.0352 | 25.3521 | 1800 | 0.1691 | 0.9401 | 0.8440 | 0.8364 | 0.8519 |
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| 0.0323 | 26.7606 | 1900 | 0.1687 | 0.9426 | 0.8509 | 0.8409 | 0.8611 |
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| 0.0297 | 28.1690 | 2000 | 0.1732 | 0.9401 | 0.8455 | 0.8304 | 0.8611 |
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| 0.0229 | 29.5775 | 2100 | 0.1712 | 0.9412 | 0.8475 | 0.8360 | 0.8593 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.5.0+cu121
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- Datasets 3.1.0
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- Tokenizers 0.19.1
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model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 343248584
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version https://git-lfs.github.com/spec/v1
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oid sha256:201cad16344a51f7b700dd64ae7718476ea3a08124be2583a936975384ebdea2
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size 343248584
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