Instructions to use hbenitez/AV_classifier1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hbenitez/AV_classifier1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hbenitez/AV_classifier1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hbenitez/AV_classifier1") model = AutoModelForImageClassification.from_pretrained("hbenitez/AV_classifier1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from hbenitez/AV_classifier1: direct link, hf CLI and curl.
- Browser
- Download file 660 Bytes
-
https://huggingface.co/hbenitez/AV_classifier1/resolve/main/config.json
- Command line
-
hf download hf://hbenitez/AV_classifier1/config.json
-
curl -L -o config.json https://huggingface.co/hbenitez/AV_classifier1/resolve/main/config.json
660 Bytes
| { | |
| "_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": "brake", | |
| "1": "dont_brake" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "brake": "0", | |
| "dont_brake": "1" | |
| }, | |
| "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, | |
| "transformers_version": "4.30.2" | |
| } | |