Feature Extraction
Transformers
Safetensors
clip_vision_model
vision
clip
fine-tuned
PatchCamelyon
medical-imaging
Instructions to use lens-ai/clip-vit-base-patch32_pcam_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lens-ai/clip-vit-base-patch32_pcam_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lens-ai/clip-vit-base-patch32_pcam_finetuned")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lens-ai/clip-vit-base-patch32_pcam_finetuned") model = AutoModel.from_pretrained("lens-ai/clip-vit-base-patch32_pcam_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "openai/clip-vit-base-patch32", | |
| "architectures": ["CLIPVisionModel"], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 32, | |
| "projection_dim": 512, | |
| "torch_dtype": "float32" | |
| } | |