feat: add tnn
Browse files- README.md +110 -0
- all_results.json +13 -0
- config.json +34 -0
- eval_results.json +8 -0
- preprocessor_config.json +22 -0
- pytorch_model.bin +3 -0
- train_results.json +8 -0
- trainer_state.json +2443 -0
- training_args.bin +3 -0
README.md
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+
---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- image-classification
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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: vit-base-tm
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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: validation
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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: 0.8883208808493905
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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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# vit-base-tm
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3039
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- Accuracy: 0.8883
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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: 128
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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 | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.8337 | 0.11 | 100 | 0.7774 | 0.6475 |
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| 0.6868 | 0.23 | 200 | 0.6481 | 0.7239 |
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| 0.6141 | 0.34 | 300 | 0.6004 | 0.7459 |
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| 71 |
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| 0.6257 | 0.46 | 400 | 0.5776 | 0.7549 |
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| 0.5603 | 0.57 | 500 | 0.5395 | 0.7766 |
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| 0.5281 | 0.69 | 600 | 0.5066 | 0.7876 |
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| 0.4781 | 0.8 | 700 | 0.4940 | 0.7918 |
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| 0.4794 | 0.91 | 800 | 0.4649 | 0.8064 |
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| 0.3345 | 1.03 | 900 | 0.4549 | 0.8167 |
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| 0.3827 | 1.14 | 1000 | 0.4284 | 0.8231 |
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| 78 |
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| 0.3415 | 1.26 | 1100 | 0.4137 | 0.8310 |
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| 0.3633 | 1.37 | 1200 | 0.3927 | 0.8384 |
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| 0.3414 | 1.49 | 1300 | 0.3922 | 0.8390 |
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| 0.3441 | 1.6 | 1400 | 0.3774 | 0.8476 |
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| 0.316 | 1.71 | 1500 | 0.3788 | 0.8475 |
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| 0.3218 | 1.83 | 1600 | 0.3580 | 0.8546 |
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| 0.2656 | 1.94 | 1700 | 0.3584 | 0.8597 |
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| 0.2005 | 2.06 | 1800 | 0.3576 | 0.8671 |
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| 0.181 | 2.17 | 1900 | 0.3426 | 0.8699 |
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| 0.2094 | 2.29 | 2000 | 0.3427 | 0.8696 |
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| 0.1831 | 2.4 | 2100 | 0.3355 | 0.8755 |
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| 0.1774 | 2.51 | 2200 | 0.3325 | 0.8793 |
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| 0.2002 | 2.63 | 2300 | 0.3211 | 0.8786 |
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| 0.1508 | 2.74 | 2400 | 0.3312 | 0.8818 |
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| 0.1669 | 2.86 | 2500 | 0.3132 | 0.8854 |
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| 0.1461 | 2.97 | 2600 | 0.3039 | 0.8883 |
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| 0.07 | 3.09 | 2700 | 0.3402 | 0.8921 |
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| 0.0637 | 3.2 | 2800 | 0.3446 | 0.8944 |
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| 0.0807 | 3.31 | 2900 | 0.3425 | 0.8947 |
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| 0.0637 | 3.43 | 3000 | 0.3396 | 0.8964 |
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| 0.0535 | 3.54 | 3100 | 0.3407 | 0.8971 |
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| 0.064 | 3.66 | 3200 | 0.3420 | 0.9002 |
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| 0.0707 | 3.77 | 3300 | 0.3314 | 0.8995 |
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| 0.058 | 3.89 | 3400 | 0.3286 | 0.9002 |
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| 0.048 | 4.0 | 3500 | 0.3263 | 0.9013 |
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### Framework versions
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- Transformers 4.33.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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all_results.json
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{
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"epoch": 4.0,
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"eval_accuracy": 0.8883208808493905,
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"eval_loss": 0.3038625717163086,
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| 5 |
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"eval_runtime": 113.9287,
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| 6 |
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"eval_samples_per_second": 133.926,
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| 7 |
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"eval_steps_per_second": 16.747,
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| 8 |
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"total_flos": 3.4689445074730156e+19,
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| 9 |
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"train_loss": 0.2980680786711829,
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| 10 |
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"train_runtime": 7553.3952,
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| 11 |
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"train_samples_per_second": 59.264,
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| 12 |
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"train_steps_per_second": 0.463
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}
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config.json
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{
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"_name_or_path": "google/vit-base-patch16-224-in21k",
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"architectures": [
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"ViTForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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| 7 |
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"encoder_stride": 16,
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"hidden_act": "gelu",
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| 9 |
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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| 11 |
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"id2label": {
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"0": "up",
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"1": "up-left",
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"2": "up-right"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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| 18 |
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"intermediate_size": 3072,
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| 19 |
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"label2id": {
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"up": "0",
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"up-left": "1",
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"up-right": "2"
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},
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| 24 |
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"layer_norm_eps": 1e-12,
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| 25 |
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"model_type": "vit",
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| 26 |
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"num_attention_heads": 12,
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| 27 |
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"num_channels": 3,
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| 28 |
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"num_hidden_layers": 12,
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"patch_size": 16,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.33.2"
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}
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eval_results.json
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{
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"epoch": 4.0,
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"eval_accuracy": 0.8883208808493905,
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| 4 |
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"eval_loss": 0.3038625717163086,
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| 5 |
+
"eval_runtime": 113.9287,
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| 6 |
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"eval_samples_per_second": 133.926,
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| 7 |
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"eval_steps_per_second": 16.747
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.5,
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0.5,
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0.5
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| 9 |
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],
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| 10 |
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"image_processor_type": "ViTFeatureExtractor",
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| 11 |
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"image_std": [
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0.5,
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| 13 |
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0.5,
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| 14 |
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0.5
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| 15 |
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],
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| 16 |
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"resample": 2,
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| 17 |
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"rescale_factor": 0.00392156862745098,
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| 18 |
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"size": {
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| 19 |
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"height": 224,
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| 20 |
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"width": 224
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| 21 |
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}
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:33deac17c3b39587f3c296ed5cb235c6c8aee5a4536ac509977f5a7369d4fc6f
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| 3 |
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size 343271789
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train_results.json
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{
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| 2 |
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"epoch": 4.0,
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| 3 |
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"total_flos": 3.4689445074730156e+19,
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| 4 |
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"train_loss": 0.2980680786711829,
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| 5 |
+
"train_runtime": 7553.3952,
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| 6 |
+
"train_samples_per_second": 59.264,
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| 7 |
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"train_steps_per_second": 0.463
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| 8 |
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}
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trainer_state.json
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training_args.bin
ADDED
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@@ -0,0 +1,3 @@
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