Instructions to use ahmedesmail16/Psoriasis-Project-M-clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Psoriasis-Project-M-clip-vit-large-patch14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Psoriasis-Project-M-clip-vit-large-patch14") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("ahmedesmail16/Psoriasis-Project-M-clip-vit-large-patch14") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Psoriasis-Project-M-clip-vit-large-patch14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -45,12 +45,22 @@ The following hyperparameters were used during training:
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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:
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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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| No log | 0.92 | 6 | 1.1418 | 0.8333 |
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| 0.0506 | 2.0 | 13 | 2.5893 | 0.7292 |
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| 0.0506 | 2.92 | 19 | 1.5925 | 0.7917 |
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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: 15
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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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| No log | 0.92 | 6 | 1.8042 | 0.7917 |
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| 0.1258 | 2.0 | 13 | 1.4387 | 0.8333 |
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| 0.1258 | 2.92 | 19 | 2.5280 | 0.7292 |
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| 0.0815 | 4.0 | 26 | 0.9424 | 0.8542 |
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| 0.0493 | 4.62 | 30 | 0.8434 | 0.8542 |
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| No log | 0.92 | 6 | 8.7831 | 0.2917 |
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| 6.4844 | 2.0 | 13 | 3.2443 | 0.5417 |
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| 6.4844 | 2.92 | 19 | 1.4924 | 0.7708 |
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| 1.5554 | 4.0 | 26 | 0.6663 | 0.875 |
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| 0.2061 | 4.62 | 30 | 0.7655 | 0.8125 |
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| No log | 0.92 | 6 | 1.1418 | 0.8333 |
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| 0.0506 | 2.0 | 13 | 2.5893 | 0.7292 |
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| 0.0506 | 2.92 | 19 | 1.5925 | 0.7917 |
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