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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/convnextv2-tiny-1k-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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+ - precision
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+ model-index:
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+ - name: convnextv2-tiny-1k-224-finetuned-pattern-v2
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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: 0.8075
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+ - name: Precision
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+ type: precision
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+ value: 0.815145699366171
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+ ---
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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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+
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+ # convnextv2-tiny-1k-224-finetuned-pattern-v2
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+
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+ This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5496
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+ - Accuracy: 0.8075
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+ - Precision: 0.8151
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 10
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+ - eval_batch_size: 4
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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: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
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+ | No log | 1.0 | 320 | 0.8829 | 0.7275 | 0.7493 |
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+ | 1.2279 | 2.0 | 640 | 0.7396 | 0.7412 | 0.7517 |
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+ | 1.2279 | 3.0 | 960 | 0.6526 | 0.7775 | 0.7902 |
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+ | 0.6811 | 4.0 | 1280 | 0.5722 | 0.7975 | 0.8076 |
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+ | 0.5073 | 5.0 | 1600 | 0.5496 | 0.8075 | 0.8151 |
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+ | 0.5073 | 6.0 | 1920 | 0.6014 | 0.7887 | 0.7991 |
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+ | 0.4098 | 7.0 | 2240 | 0.5759 | 0.8125 | 0.8171 |
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+ | 0.3357 | 8.0 | 2560 | 0.6241 | 0.7987 | 0.8126 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
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+ - Pytorch 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
all_results.json ADDED
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+ {
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+ "epoch": 8.0,
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+ "eval_accuracy": 0.8075,
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+ "eval_loss": 0.5496437549591064,
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+ "eval_precision": 0.815145699366171,
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+ "eval_runtime": 15.0278,
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+ "eval_samples_per_second": 53.235,
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+ "eval_steps_per_second": 13.309
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "facebook/convnextv2-tiny-1k-224",
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+ "architectures": [
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+ "ConvNextV2ForImageClassification"
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+ ],
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+ "depths": [
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+ 3,
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+ 3,
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+ 9,
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+ 3
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+ ],
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+ "drop_path_rate": 0.0,
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+ "hidden_act": "gelu",
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+ "hidden_sizes": [
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+ 96,
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+ 192,
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+ 384,
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+ 768
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+ ],
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+ "id2label": {
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+ "0": "Abstract_Pattern",
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+ "1": "Check_Pattern",
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+ "2": "Color_Block_Pattern",
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+ "3": "Embellished_Pattern",
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+ "4": "Embroider_Pattern",
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+ "5": "Geometric_pattern",
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+ "6": "Graphic_Pattern",
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+ "7": "Lace_Pattern"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "Abstract_Pattern": 0,
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+ "Check_Pattern": 1,
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+ "Color_Block_Pattern": 2,
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+ "Embellished_Pattern": 3,
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+ "Embroider_Pattern": 4,
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+ "Geometric_pattern": 5,
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+ "Graphic_Pattern": 6,
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+ "Lace_Pattern": 7
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "convnextv2",
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+ "num_channels": 3,
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+ "num_stages": 4,
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+ "out_features": [
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+ "stage4"
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+ ],
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+ "out_indices": [
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+ 4
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+ ],
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+ "patch_size": 4,
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+ "problem_type": "single_label_classification",
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+ "stage_names": [
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+ "stem",
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+ "stage1",
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+ "stage2",
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+ "stage3",
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+ "stage4"
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+ ],
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.44.0"
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+ }
eval_results.json ADDED
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+ {
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+ "epoch": 8.0,
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+ "eval_accuracy": 0.8075,
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+ "eval_loss": 0.5496437549591064,
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+ "eval_precision": 0.815145699366171,
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+ "eval_runtime": 15.0278,
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+ "eval_samples_per_second": 53.235,
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+ "eval_steps_per_second": 13.309
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+ }
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+ "do_normalize": true,
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+ "do_rescale": true,
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+ "image_mean": [
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+ "image_processor_type": "ConvNextImageProcessor",
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+ "image_std": [
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