Image Classification
Transformers
Safetensors
vit
vision
defect-detection
manufacturing-quality-control
Generated from Trainer
Eval Results (legacy)
Instructions to use Dongjin1203/defect-classifier-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dongjin1203/defect-classifier-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dongjin1203/defect-classifier-vit-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Dongjin1203/defect-classifier-vit-base") model = AutoModelForImageClassification.from_pretrained("Dongjin1203/defect-classifier-vit-base") - Notebooks
- Google Colab
- Kaggle
File size: 736 Bytes
8b8975f | 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 | {
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"dtype": "float32",
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "good",
"1": "defect"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"defect": 1,
"good": 0
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"pooler_act": "tanh",
"pooler_output_size": 768,
"problem_type": "single_label_classification",
"qkv_bias": true,
"transformers_version": "5.12.1",
"use_cache": false
}
|