Instructions to use slotwhisperer/symbols-mnv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slotwhisperer/symbols-mnv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="slotwhisperer/symbols-mnv3") 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("slotwhisperer/symbols-mnv3") model = AutoModelForImageClassification.from_pretrained("slotwhisperer/symbols-mnv3", device_map="auto") - timm
How to use slotwhisperer/symbols-mnv3 with timm:
import timm model = timm.create_model("hf_hub:slotwhisperer/symbols-mnv3", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,284 Bytes
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"architecture": "mobilenetv3_small_100",
"architectures": [
"TimmWrapperForImageClassification"
],
"do_pooling": true,
"dtype": "float32",
"initializer_range": 0.02,
"label_names": [
"10",
"9",
"ACE",
"BLACK_DIAMOND_NO_NUDGE",
"BLACK_DIAMOND_NUDGE_DOWN",
"BONUS_RETRIGGERED",
"DOUBLE_BAR",
"DUDE",
"FAN",
"FOUNTAIN",
"GOLD_BIRD",
"GOLD_COIN",
"GOLD_COIN_BONUS",
"GOLD_COIN_NO_BONUS",
"GOLD_DRAGON",
"GOLD_SHIP",
"GOLD_TURTLE",
"JACK",
"KING",
"LADY",
"QUEEN",
"VASE",
"WILD",
"YING_YANG"
],
"model_args": null,
"model_type": "timm_wrapper",
"num_classes": 24,
"num_features": 1024,
"pretrained_cfg": {
"classifier": "classifier",
"crop_mode": "center",
"crop_pct": 0.875,
"custom_load": false,
"first_conv": "conv_stem",
"fixed_input_size": false,
"input_size": [
3,
224,
224
],
"interpolation": "bicubic",
"mean": [
0.485,
0.456,
0.406
],
"pool_size": [
7,
7
],
"std": [
0.229,
0.224,
0.225
],
"tag": "lamb_in1k"
},
"problem_type": "single_label_classification",
"transformers_version": "5.8.1",
"use_cache": false
}
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