Instructions to use SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft") model = AutoModelForImageClassification.from_pretrained("SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +122 -0
- config.json +63 -0
- logs/events.out.tfevents.1745554756.c6bc1958280e.1138.0 +3 -0
- model.safetensors +3 -0
- preprocessor_config.json +22 -0
- training_args.bin +3 -0
- training_metrics.xlsx +0 -0
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: facebook/convnextv2-base-1k-224
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft
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results: []
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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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# convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft
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This model is a fine-tuned version of [facebook/convnextv2-base-1k-224](https://huggingface.co/facebook/convnextv2-base-1k-224) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6774
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- Accuracy: 0.7819
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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: 0.0003
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine_with_restarts
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- lr_scheduler_warmup_steps: 256
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 2.0691 | 0.5 | 64 | 2.0083 | 0.3523 |
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| 1.9566 | 1.0 | 128 | 1.8732 | 0.5201 |
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| 1.7715 | 1.5 | 192 | 1.6903 | 0.5638 |
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| 1.5752 | 2.0 | 256 | 1.5040 | 0.6074 |
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| 1.4088 | 2.5 | 320 | 1.3569 | 0.6208 |
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| 1.2927 | 3.0 | 384 | 1.2600 | 0.6309 |
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| 1.2173 | 3.5 | 448 | 1.1948 | 0.6577 |
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| 1.1515 | 4.0 | 512 | 1.1464 | 0.6644 |
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| 1.1183 | 4.5 | 576 | 1.1160 | 0.6711 |
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| 1.0893 | 5.0 | 640 | 1.1001 | 0.6879 |
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| 1.0792 | 5.5 | 704 | 1.0898 | 0.6913 |
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| 1.0627 | 6.0 | 768 | 1.0831 | 0.6846 |
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| 1.0714 | 6.5 | 832 | 1.0817 | 0.6846 |
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| 1.0459 | 7.0 | 896 | 1.0483 | 0.6913 |
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| 1.0282 | 7.5 | 960 | 1.0047 | 0.6980 |
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| 0.9605 | 8.0 | 1024 | 0.9774 | 0.7081 |
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| 0.9405 | 8.5 | 1088 | 0.9489 | 0.7114 |
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| 0.9316 | 9.0 | 1152 | 0.9353 | 0.7148 |
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| 0.9174 | 9.5 | 1216 | 0.9208 | 0.7181 |
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| 0.8924 | 10.0 | 1280 | 0.9137 | 0.7215 |
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| 0.9009 | 10.5 | 1344 | 0.9101 | 0.7282 |
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| 0.8844 | 11.0 | 1408 | 0.9092 | 0.7248 |
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| 0.8873 | 11.5 | 1472 | 0.9076 | 0.7215 |
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| 0.8751 | 12.0 | 1536 | 0.8721 | 0.7383 |
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| 0.8553 | 12.5 | 1600 | 0.8617 | 0.7248 |
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| 0.8265 | 13.0 | 1664 | 0.8428 | 0.7416 |
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| 0.8133 | 13.5 | 1728 | 0.8302 | 0.7416 |
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| 0.808 | 14.0 | 1792 | 0.8232 | 0.7483 |
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| 0.7915 | 14.5 | 1856 | 0.8187 | 0.7450 |
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| 0.7975 | 15.0 | 1920 | 0.8157 | 0.7450 |
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| 0.7765 | 15.5 | 1984 | 0.8143 | 0.7450 |
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| 0.8017 | 16.0 | 2048 | 0.8142 | 0.7450 |
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| 0.793 | 16.5 | 2112 | 0.7970 | 0.7584 |
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| 0.7567 | 17.0 | 2176 | 0.7901 | 0.7550 |
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| 0.7576 | 17.5 | 2240 | 0.7785 | 0.7483 |
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| 0.7377 | 18.0 | 2304 | 0.7651 | 0.7651 |
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| 0.7311 | 18.5 | 2368 | 0.7588 | 0.7651 |
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| 0.7276 | 19.0 | 2432 | 0.7566 | 0.7651 |
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| 0.7237 | 19.5 | 2496 | 0.7567 | 0.7651 |
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| 0.7171 | 20.0 | 2560 | 0.7534 | 0.7685 |
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| 0.7158 | 20.5 | 2624 | 0.7529 | 0.7685 |
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| 0.7188 | 21.0 | 2688 | 0.7486 | 0.7651 |
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| 0.7112 | 21.5 | 2752 | 0.7340 | 0.7752 |
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| 0.6912 | 22.0 | 2816 | 0.7297 | 0.7752 |
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| 0.6784 | 22.5 | 2880 | 0.7229 | 0.7785 |
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| 0.6868 | 23.0 | 2944 | 0.7152 | 0.7752 |
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| 0.6701 | 23.5 | 3008 | 0.7132 | 0.7819 |
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| 0.6718 | 24.0 | 3072 | 0.7111 | 0.7752 |
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| 0.671 | 24.5 | 3136 | 0.7105 | 0.7785 |
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| 0.6609 | 25.0 | 3200 | 0.7097 | 0.7785 |
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| 0.6722 | 25.5 | 3264 | 0.7066 | 0.7785 |
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| 0.6526 | 26.0 | 3328 | 0.6959 | 0.7785 |
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| 0.6448 | 26.5 | 3392 | 0.6920 | 0.7919 |
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| 0.6493 | 27.0 | 3456 | 0.6903 | 0.7785 |
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| 0.6394 | 27.5 | 3520 | 0.6816 | 0.7785 |
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| 0.6274 | 28.0 | 3584 | 0.6819 | 0.7819 |
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| 0.6198 | 28.5 | 3648 | 0.6784 | 0.7819 |
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| 0.632 | 29.0 | 3712 | 0.6778 | 0.7819 |
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| 0.634 | 29.5 | 3776 | 0.6776 | 0.7819 |
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| 0.612 | 30.0 | 3840 | 0.6774 | 0.7819 |
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### Framework versions
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- Transformers 4.48.3
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- Pytorch 2.5.1+cu124
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- Datasets 3.3.2
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- Tokenizers 0.21.1
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config.json
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{
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"_name_or_path": "facebook/convnextv2-base-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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27,
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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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128,
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256,
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512,
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1024
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],
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"id2label": {
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"0": "Bacterial Leaf Blight",
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"1": "Brown Spot",
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"2": "Healthy Rice Leaf",
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"3": "Leaf Blast",
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"4": "Leaf scald",
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"5": "Narrow Brown Leaf Spot",
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"6": "Rice Hispa",
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"7": "Sheath Blight"
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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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"Bacterial Leaf Blight": 0,
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"Brown Spot": 1,
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"Healthy Rice Leaf": 2,
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"Leaf Blast": 3,
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"Leaf scald": 4,
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"Narrow Brown Leaf Spot": 5,
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"Rice Hispa": 6,
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"Sheath Blight": 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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| 45 |
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"num_stages": 4,
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"out_features": [
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"stage4"
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| 48 |
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],
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| 49 |
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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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| 54 |
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"stage_names": [
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"stem",
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"stage1",
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| 57 |
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"stage2",
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| 58 |
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"stage3",
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| 59 |
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"stage4"
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| 60 |
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],
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| 61 |
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"torch_dtype": "float32",
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| 62 |
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"transformers_version": "4.48.3"
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| 63 |
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}
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logs/events.out.tfevents.1745554756.c6bc1958280e.1138.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:463c0c5fbcbe4a4acdb4e61e6432de19a78294f651531b8c65e4fa46ba457543
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size 44007
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ebc571ca38433d131a5ca7646997b1e1e6fbd5e49b46f2ca0c63d1b417afad82
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size 350850048
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preprocessor_config.json
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{
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"crop_pct": 0.875,
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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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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ConvNextImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 3,
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| 18 |
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"rescale_factor": 0.00392156862745098,
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| 19 |
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"size": {
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| 20 |
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"shortest_edge": 224
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| 21 |
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}
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| 22 |
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:37f36e97d21e9708911a588db16f86194317d2c15c60c3f56b2ac19458e9a12d
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size 5496
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training_metrics.xlsx
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Binary file (11.1 kB). View file
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