Image Classification
Transformers
Safetensors
total_classifier
feature-extraction
radiology
ct
organ
classification
custom_code
Instructions to use ianpan/total-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/total-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ianpan/total-classifier", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ianpan/total-classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "TotalClassifierModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration.TotalClassifierConfig", | |
| "AutoModel": "modeling.TotalClassifierModel" | |
| }, | |
| "backbone": "tf_efficientnetv2_b0", | |
| "cnn_dropout": 0.1, | |
| "feature_dim": 192, | |
| "image_size": [ | |
| 256, | |
| 256 | |
| ], | |
| "in_chans": 1, | |
| "linear_dropout": 0.1, | |
| "model_type": "total_classifier", | |
| "num_classes": 117, | |
| "rnn_dropout": 0.0, | |
| "rnn_num_layers": 1, | |
| "rnn_type": "GRU", | |
| "seq_len": 512, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0" | |
| } | |