Instructions to use hf-internal-testing/tiny-random-CLIPForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-CLIPForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-CLIPForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPForImageClassification") - Notebooks
- Google Colab
- Kaggle
File size: 952 Bytes
4a20306 0f6d715 4a20306 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"architectures": [
"CLIPForImageClassification"
],
"bos_token_id": 0,
"eos_token_id": 1,
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "clip",
"pad_token_id": 1,
"projection_dim": 64,
"text_config": {
"attention_dropout": 0.1,
"bos_token_id": 0,
"dropout": 0.1,
"eos_token_id": 1,
"hidden_size": 32,
"intermediate_size": 37,
"max_position_embeddings": 512,
"model_type": "clip_text_model",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"projection_dim": 32,
"vocab_size": 1024
},
"torch_dtype": "float32",
"transformers_version": "4.40.0.dev0",
"vision_config": {
"attention_dropout": 0.1,
"dropout": 0.1,
"hidden_size": 32,
"image_size": 30,
"intermediate_size": 37,
"model_type": "clip_vision_model",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"patch_size": 2,
"projection_dim": 32
}
}
|