Instructions to use HalogenFlo/vit-emnist-byclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HalogenFlo/vit-emnist-byclass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HalogenFlo/vit-emnist-byclass") 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("HalogenFlo/vit-emnist-byclass") model = AutoModelForImageClassification.from_pretrained("HalogenFlo/vit-emnist-byclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ViTForImageClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "dtype": "float32", | |
| "encoder_stride": 16, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "0", | |
| "1": "1", | |
| "2": "2", | |
| "3": "3", | |
| "4": "4", | |
| "5": "5", | |
| "6": "6", | |
| "7": "7", | |
| "8": "8", | |
| "9": "9", | |
| "10": "A", | |
| "11": "B", | |
| "12": "C", | |
| "13": "D", | |
| "14": "E", | |
| "15": "F", | |
| "16": "G", | |
| "17": "H", | |
| "18": "I", | |
| "19": "J", | |
| "20": "K", | |
| "21": "L", | |
| "22": "M", | |
| "23": "N", | |
| "24": "O", | |
| "25": "P", | |
| "26": "Q", | |
| "27": "R", | |
| "28": "S", | |
| "29": "T", | |
| "30": "U", | |
| "31": "V", | |
| "32": "W", | |
| "33": "X", | |
| "34": "Y", | |
| "35": "Z", | |
| "36": "a", | |
| "37": "b", | |
| "38": "c", | |
| "39": "d", | |
| "40": "e", | |
| "41": "f", | |
| "42": "g", | |
| "43": "h", | |
| "44": "i", | |
| "45": "j", | |
| "46": "k", | |
| "47": "l", | |
| "48": "m", | |
| "49": "n", | |
| "50": "o", | |
| "51": "p", | |
| "52": "q", | |
| "53": "r", | |
| "54": "s", | |
| "55": "t", | |
| "56": "u", | |
| "57": "v", | |
| "58": "w", | |
| "59": "x", | |
| "60": "y", | |
| "61": "z" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "0": 0, | |
| "1": 1, | |
| "2": 2, | |
| "3": 3, | |
| "4": 4, | |
| "5": 5, | |
| "6": 6, | |
| "7": 7, | |
| "8": 8, | |
| "9": 9, | |
| "A": 10, | |
| "B": 11, | |
| "C": 12, | |
| "D": 13, | |
| "E": 14, | |
| "F": 15, | |
| "G": 16, | |
| "H": 17, | |
| "I": 18, | |
| "J": 19, | |
| "K": 20, | |
| "L": 21, | |
| "M": 22, | |
| "N": 23, | |
| "O": 24, | |
| "P": 25, | |
| "Q": 26, | |
| "R": 27, | |
| "S": 28, | |
| "T": 29, | |
| "U": 30, | |
| "V": 31, | |
| "W": 32, | |
| "X": 33, | |
| "Y": 34, | |
| "Z": 35, | |
| "a": 36, | |
| "b": 37, | |
| "c": 38, | |
| "d": 39, | |
| "e": 40, | |
| "f": 41, | |
| "g": 42, | |
| "h": 43, | |
| "i": 44, | |
| "j": 45, | |
| "k": 46, | |
| "l": 47, | |
| "m": 48, | |
| "n": 49, | |
| "o": 50, | |
| "p": 51, | |
| "q": 52, | |
| "r": 53, | |
| "s": 54, | |
| "t": 55, | |
| "u": 56, | |
| "v": 57, | |
| "w": 58, | |
| "x": 59, | |
| "y": 60, | |
| "z": 61 | |
| }, | |
| "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, | |
| "qkv_bias": true, | |
| "transformers_version": "4.57.2" | |
| } | |