Instructions to use buddhadeb33/output_dinov2_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buddhadeb33/output_dinov2_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="buddhadeb33/output_dinov2_large") 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("buddhadeb33/output_dinov2_large") model = AutoModelForImageClassification.from_pretrained("buddhadeb33/output_dinov2_large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,444 Bytes
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"apply_layernorm": true,
"architectures": [
"Dinov2ForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"drop_path_rate": 0.0,
"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1024,
"id2label": {
"0": "Pano",
"1": "FMX",
"2": "BW",
"3": "PA",
"4": "PC",
"5": "IOP",
"6": "Photo",
"7": "NA"
},
"image_size": 518,
"initializer_range": 0.02,
"label2id": {
"BW": 2,
"FMX": 1,
"IOP": 5,
"NA": 7,
"PA": 3,
"PC": 4,
"Pano": 0,
"Photo": 6
},
"layer_norm_eps": 1e-06,
"layerscale_value": 1.0,
"mlp_ratio": 4,
"model_type": "dinov2",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"out_features": [
"stage24"
],
"out_indices": [
24
],
"patch_size": 14,
"problem_type": "multi_label_classification",
"qkv_bias": true,
"reshape_hidden_states": true,
"stage_names": [
"stem",
"stage1",
"stage2",
"stage3",
"stage4",
"stage5",
"stage6",
"stage7",
"stage8",
"stage9",
"stage10",
"stage11",
"stage12",
"stage13",
"stage14",
"stage15",
"stage16",
"stage17",
"stage18",
"stage19",
"stage20",
"stage21",
"stage22",
"stage23",
"stage24"
],
"transformers_version": "5.0.0",
"use_cache": false,
"use_mask_token": true,
"use_swiglu_ffn": false
}
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