Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use malifiahm/vehicle_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malifiahm/vehicle_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="malifiahm/vehicle_classification") 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("malifiahm/vehicle_classification") model = AutoModelForImageClassification.from_pretrained("malifiahm/vehicle_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from malifiahm/vehicle_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/malifiahm/vehicle_classification/resolve/main/config.json
- Command line
-
hf download hf://malifiahm/vehicle_classification/config.json
-
curl -L -o config.json https://huggingface.co/malifiahm/vehicle_classification/resolve/main/config.json
1.33 kB
| { | |
| "_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": "Ambulance", | |
| "1": "Barge", | |
| "10": "Motorcycle", | |
| "11": "Segway", | |
| "12": "Snowmobile", | |
| "13": "Tank", | |
| "14": "Taxi", | |
| "15": "Truck", | |
| "16": "Van", | |
| "2": "Bicycle", | |
| "3": "Boat", | |
| "4": "Bus", | |
| "5": "Car", | |
| "6": "Cart", | |
| "7": "Caterpillar", | |
| "8": "Helicopter", | |
| "9": "Limousine" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Ambulance": "0", | |
| "Barge": "1", | |
| "Bicycle": "2", | |
| "Boat": "3", | |
| "Bus": "4", | |
| "Car": "5", | |
| "Cart": "6", | |
| "Caterpillar": "7", | |
| "Helicopter": "8", | |
| "Limousine": "9", | |
| "Motorcycle": "10", | |
| "Segway": "11", | |
| "Snowmobile": "12", | |
| "Tank": "13", | |
| "Taxi": "14", | |
| "Truck": "15", | |
| "Van": "16" | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "vit", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 16, | |
| "problem_type": "single_label_classification", | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.2" | |
| } | |