Instructions to use Pro-Coder/skin-lesion-vit-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pro-Coder/skin-lesion-vit-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Pro-Coder/skin-lesion-vit-finetuned") 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("Pro-Coder/skin-lesion-vit-finetuned") model = AutoModelForImageClassification.from_pretrained("Pro-Coder/skin-lesion-vit-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "google/vit-base-patch16-224-in21k", | |
| "architectures": [ | |
| "ViTForImageClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "encoder_stride": 16, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Actinic keratoses", | |
| "1": "Basal cell carcinoma", | |
| "2": "Benign keratosis-like lesions", | |
| "3": "Dermatofibroma", | |
| "4": "Melanocytic nevi", | |
| "5": "Melanoma", | |
| "6": "Vascular lesions" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "actinic_keratoses": 0, | |
| "basal_cell_carcinoma": 1, | |
| "benign_keratosis-like_lesions": 2, | |
| "dermatofibroma": 3, | |
| "melanocytic_Nevi": 4, | |
| "melanoma": 5, | |
| "vascular_lesions": 6 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "vit", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 16, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2" | |
| } | |