Instructions to use hf-internal-testing/tiny-random-Owlv2ForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Owlv2ForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="hf-internal-testing/tiny-random-Owlv2ForObjectDetection")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Owlv2ForObjectDetection") model = AutoModelForZeroShotObjectDetection.from_pretrained("hf-internal-testing/tiny-random-Owlv2ForObjectDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 790 Bytes
30439c0 | 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 32 33 34 35 | {
"architectures": [
"Owlv2ForObjectDetection"
],
"bos_token_id": 1,
"eos_token_id": 2,
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "owlv2",
"pad_token_id": 0,
"projection_dim": 64,
"text_config": {
"attention_dropout": 0.1,
"dropout": 0.1,
"hidden_size": 64,
"intermediate_size": 37,
"model_type": "owlv2_text_model",
"num_attention_heads": 4,
"vocab_size": 1024
},
"torch_dtype": "float32",
"transformers_version": "4.36.0.dev0",
"vision_config": {
"attention_dropout": 0.1,
"dropout": 0.1,
"hidden_size": 32,
"image_size": 32,
"intermediate_size": 37,
"model_type": "owlv2_vision_model",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"patch_size": 2
}
}
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