Instructions to use vlevi/Main_Fashion-convnext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vlevi/Main_Fashion-convnext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vlevi/Main_Fashion-convnext") 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("vlevi/Main_Fashion-convnext") model = AutoModelForImageClassification.from_pretrained("vlevi/Main_Fashion-convnext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +74 -0
- model.safetensors +1 -1
README.md
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---
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license: apache-2.0
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base_model: facebook/convnext-tiny-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: Main_Fashion-convnext
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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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# Main_Fashion-convnext
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1758
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- Accuracy: 0.6381
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 12
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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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| 2.0951 | 0.9630 | 13 | 2.0201 | 0.2251 |
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| 1.9821 | 2.0 | 27 | 1.8213 | 0.4037 |
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| 1.7245 | 2.9630 | 40 | 1.6774 | 0.4640 |
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| 1.6117 | 4.0 | 54 | 1.5480 | 0.5452 |
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| 1.5 | 4.9630 | 67 | 1.4506 | 0.5615 |
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| 1.3393 | 6.0 | 81 | 1.3610 | 0.5963 |
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| 1.2579 | 6.9630 | 94 | 1.2995 | 0.6172 |
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| 1.2405 | 8.0 | 108 | 1.2480 | 0.6288 |
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| 1.1479 | 8.9630 | 121 | 1.2127 | 0.6357 |
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| 1.1005 | 10.0 | 135 | 1.1898 | 0.6381 |
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| 1.0989 | 10.9630 | 148 | 1.1778 | 0.6381 |
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| 1.0816 | 11.5556 | 156 | 1.1758 | 0.6381 |
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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-
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size 111326392
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version https://git-lfs.github.com/spec/v1
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size 111326392
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