Instructions to use jhoppanne/SkinCancerClassifier_Plain-V0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhoppanne/SkinCancerClassifier_Plain-V0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jhoppanne/SkinCancerClassifier_Plain-V0") 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("jhoppanne/SkinCancerClassifier_Plain-V0") model = AutoModelForImageClassification.from_pretrained("jhoppanne/SkinCancerClassifier_Plain-V0") - Notebooks
- Google Colab
- Kaggle
SkinCancerClassifier_Plain-V0
This model is a fine-tuned version of microsoft/resnet-152 on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 3.2075
- eval_accuracy: 0.6083
- eval_runtime: 1.756
- eval_samples_per_second: 136.671
- eval_steps_per_second: 4.556
- epoch: 250.6
- step: 7518
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
Framework versions
- Transformers 4.42.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.19.1
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Model tree for jhoppanne/SkinCancerClassifier_Plain-V0
Base model
microsoft/resnet-152