Instructions to use SaladSlayer00/image_classification_resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaladSlayer00/image_classification_resnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SaladSlayer00/image_classification_resnet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("SaladSlayer00/image_classification_resnet") model = AutoModelForImageClassification.from_pretrained("SaladSlayer00/image_classification_resnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 158a5b8
Training in progress epoch 1
Browse files
README.md
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss:
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- Validation Loss: 4.
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- Validation Accuracy: 0.
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- Epoch:
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## Model description
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| Train Loss | Validation Loss | Validation Accuracy | Epoch |
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|:----------:|:---------------:|:-------------------:|:-----:|
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| 7.0880 | 4.8638 | 0.0146 | 0 |
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### Framework versions
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 4.6502
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- Validation Loss: 4.5469
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- Validation Accuracy: 0.0694
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- Epoch: 1
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## Model description
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| Train Loss | Validation Loss | Validation Accuracy | Epoch |
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|:----------:|:---------------:|:-------------------:|:-----:|
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| 7.0880 | 4.8638 | 0.0146 | 0 |
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| 4.6502 | 4.5469 | 0.0694 | 1 |
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### Framework versions
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logs/train/events.out.tfevents.1703538427.fv-az847-406.1850.0.v2
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logs/validation/events.out.tfevents.1703540000.fv-az847-406.1850.1.v2
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tf_model.h5
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