Instructions to use joelg/sam-vit-base-cotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joelg/sam-vit-base-cotes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="joelg/sam-vit-base-cotes")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("joelg/sam-vit-base-cotes") model = AutoModelForMaskGeneration.from_pretrained("joelg/sam-vit-base-cotes") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "SamModel" | |
| ], | |
| "dtype": "float32", | |
| "initializer_range": 0.02, | |
| "mask_decoder_config": { | |
| "add_cross_attention": false, | |
| "attention_downsample_rate": 2, | |
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| "cross_attention_hidden_size": null, | |
| "decoder_start_token_id": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "finetuning_task": null, | |
| "hidden_act": "relu", | |
| "hidden_size": 256, | |
| "iou_head_depth": 3, | |
| "iou_head_hidden_dim": 256, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_dim": 2048, | |
| "model_type": "", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 2, | |
| "num_multimask_outputs": 3, | |
| "pad_token_id": null, | |
| "prefix": null, | |
| "pruned_heads": {}, | |
| "sep_token_id": null, | |
| "task_specific_params": null, | |
| "tf_legacy_loss": false, | |
| "tie_encoder_decoder": false, | |
| "tie_word_embeddings": true, | |
| "tokenizer_class": null, | |
| "torchscript": false, | |
| "use_bfloat16": false | |
| }, | |
| "model_type": "sam", | |
| "prompt_encoder_config": { | |
| "add_cross_attention": false, | |
| "bos_token_id": null, | |
| "cross_attention_hidden_size": null, | |
| "decoder_start_token_id": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "finetuning_task": null, | |
| "hidden_act": "gelu", | |
| "hidden_size": 256, | |
| "image_embedding_size": 64, | |
| "image_size": 1024, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-06, | |
| "mask_input_channels": 16, | |
| "model_type": "", | |
| "num_point_embeddings": 4, | |
| "pad_token_id": null, | |
| "patch_size": 16, | |
| "prefix": null, | |
| "pruned_heads": {}, | |
| "sep_token_id": null, | |
| "task_specific_params": null, | |
| "tf_legacy_loss": false, | |
| "tie_encoder_decoder": false, | |
| "tie_word_embeddings": true, | |
| "tokenizer_class": null, | |
| "torchscript": false, | |
| "use_bfloat16": false | |
| }, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.9.0", | |
| "use_cache": false, | |
| "vision_config": { | |
| "add_cross_attention": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": null, | |
| "cross_attention_hidden_size": null, | |
| "decoder_start_token_id": null, | |
| "dropout": 0.0, | |
| "dtype": "float32", | |
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| "finetuning_task": null, | |
| "global_attn_indexes": [ | |
| 2, | |
| 5, | |
| 8, | |
| 11 | |
| ], | |
| "hidden_act": "gelu", | |
| "hidden_size": 768, | |
| "image_size": 1024, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 1e-10, | |
| "intermediate_size": 6144, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_dim": 3072, | |
| "mlp_ratio": 4.0, | |
| "model_type": "sam_vision_model", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "num_pos_feats": 128, | |
| "output_channels": 256, | |
| "pad_token_id": null, | |
| "patch_size": 16, | |
| "prefix": null, | |
| "projection_dim": 512, | |
| "pruned_heads": {}, | |
| "qkv_bias": true, | |
| "scale": 384, | |
| "sep_token_id": null, | |
| "task_specific_params": null, | |
| "tf_legacy_loss": false, | |
| "tie_encoder_decoder": false, | |
| "tie_word_embeddings": true, | |
| "tokenizer_class": null, | |
| "torchscript": false, | |
| "use_abs_pos": true, | |
| "use_bfloat16": false, | |
| "use_rel_pos": true, | |
| "window_size": 14 | |
| } | |
| } | |