Instructions to use hf-internal-testing/tiny-random-SegGptModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegGptModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SegGptModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-SegGptModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SegGptModel") - Notebooks
- Google Colab
- Kaggle
File size: 660 Bytes
0fc4261 7fc5acd 0fc4261 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"architectures": [
"SegGptModel"
],
"beta": 0.01,
"decoder_hidden_size": 10,
"drop_path_rate": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 32,
"image_size": 30,
"initializer_range": 0.02,
"intermediate_hidden_state_indices": [
1
],
"layer_norm_eps": 1e-06,
"merge_index": 0,
"mlp_dim": 128,
"mlp_ratio": 2.0,
"model_type": "seggpt",
"num_attention_heads": 4,
"num_channels": 3,
"num_hidden_layers": 2,
"patch_size": 2,
"pretrain_image_size": 10,
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.40.0.dev0",
"use_relative_position_embeddings": true
}
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