Instructions to use Mattysmittttt/camonet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mattysmittttt/camonet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mattysmittttt/camonet") 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("Mattysmittttt/camonet") model = AutoModelForImageClassification.from_pretrained("Mattysmittttt/camonet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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": 384, | |
| "id2label": { | |
| "0": "us_erdl", | |
| "1": "us_m81_woodland", | |
| "2": "us_dcu_chocolate_chip", | |
| "3": "us_dcu_3color", | |
| "4": "us_marpat_woodland", | |
| "5": "us_marpat_desert", | |
| "6": "us_ucp", | |
| "7": "us_multicam", | |
| "8": "us_ocp_scorpion", | |
| "9": "us_aor1", | |
| "10": "us_aor2", | |
| "11": "us_tigerstripe", | |
| "12": "uk_dpm_woodland", | |
| "13": "uk_dpm_desert", | |
| "14": "uk_mtp", | |
| "15": "de_flecktarn", | |
| "16": "de_tropentarn", | |
| "17": "de_splittertarn", | |
| "18": "ru_klmk", | |
| "19": "ru_ttsko", | |
| "20": "ru_vsr_93", | |
| "21": "ru_emr_digital_flora", | |
| "22": "ru_surpat", | |
| "23": "ru_partizan", | |
| "24": "ca_cadpat_tw", | |
| "25": "ca_cadpat_ar", | |
| "26": "fr_cce", | |
| "27": "fr_daguet", | |
| "28": "it_vegetata", | |
| "29": "au_auscam", | |
| "30": "au_amcu", | |
| "31": "se_m90", | |
| "32": "ch_taz_90", | |
| "33": "no_m75", | |
| "34": "cn_type07_universal", | |
| "35": "cn_type07_desert", | |
| "36": "kr_granite", | |
| "37": "jp_jgsdf", | |
| "38": "commercial_kryptek_mandrake", | |
| "39": "commercial_atacs_au" | |
| }, | |
| "image_size": 518, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "au_amcu": 30, | |
| "au_auscam": 29, | |
| "ca_cadpat_ar": 25, | |
| "ca_cadpat_tw": 24, | |
| "ch_taz_90": 32, | |
| "cn_type07_desert": 35, | |
| "cn_type07_universal": 34, | |
| "commercial_atacs_au": 39, | |
| "commercial_kryptek_mandrake": 38, | |
| "de_flecktarn": 15, | |
| "de_splittertarn": 17, | |
| "de_tropentarn": 16, | |
| "fr_cce": 26, | |
| "fr_daguet": 27, | |
| "it_vegetata": 28, | |
| "jp_jgsdf": 37, | |
| "kr_granite": 36, | |
| "no_m75": 33, | |
| "ru_emr_digital_flora": 21, | |
| "ru_klmk": 18, | |
| "ru_partizan": 23, | |
| "ru_surpat": 22, | |
| "ru_ttsko": 19, | |
| "ru_vsr_93": 20, | |
| "se_m90": 31, | |
| "uk_dpm_desert": 13, | |
| "uk_dpm_woodland": 12, | |
| "uk_mtp": 14, | |
| "us_aor1": 9, | |
| "us_aor2": 10, | |
| "us_dcu_3color": 3, | |
| "us_dcu_chocolate_chip": 2, | |
| "us_erdl": 0, | |
| "us_m81_woodland": 1, | |
| "us_marpat_desert": 5, | |
| "us_marpat_woodland": 4, | |
| "us_multicam": 7, | |
| "us_ocp_scorpion": 8, | |
| "us_tigerstripe": 11, | |
| "us_ucp": 6 | |
| }, | |
| "layer_norm_eps": 1e-06, | |
| "layerscale_value": 1.0, | |
| "mlp_ratio": 4, | |
| "model_type": "dinov2", | |
| "num_attention_heads": 6, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "out_features": [ | |
| "stage12" | |
| ], | |
| "out_indices": [ | |
| 12 | |
| ], | |
| "patch_size": 14, | |
| "problem_type": "single_label_classification", | |
| "qkv_bias": true, | |
| "reshape_hidden_states": true, | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4", | |
| "stage5", | |
| "stage6", | |
| "stage7", | |
| "stage8", | |
| "stage9", | |
| "stage10", | |
| "stage11", | |
| "stage12" | |
| ], | |
| "transformers_version": "5.7.0", | |
| "use_cache": false, | |
| "use_mask_token": true, | |
| "use_swiglu_ffn": false | |
| } | |