Instructions to use Mo0310/5242_scratch_wbc100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mo0310/5242_scratch_wbc100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mo0310/5242_scratch_wbc100") 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("Mo0310/5242_scratch_wbc100") model = AutoModelForImageClassification.from_pretrained("Mo0310/5242_scratch_wbc100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ViTForImageClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "decoder_hidden_size": 512, | |
| "decoder_intermediate_size": 2048, | |
| "decoder_num_attention_heads": 16, | |
| "decoder_num_hidden_layers": 8, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Basophil", | |
| "1": "Eosinophil", | |
| "2": "Lymphocyte", | |
| "3": "Monocyte", | |
| "4": "Neutrophil" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Basophil": 0, | |
| "Eosinophil": 1, | |
| "Lymphocyte": 2, | |
| "Monocyte": 3, | |
| "Neutrophil": 4 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "mask_ratio": 0.75, | |
| "model_type": "vit_mae", | |
| "norm_pix_loss": false, | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 16, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.34.1" | |
| } | |