Instructions to use krasserm/perceiver-io-optical-flow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krasserm/perceiver-io-optical-flow with Transformers:
# Load model directly from transformers import OpticalFlowPerceiver model = OpticalFlowPerceiver.from_pretrained("krasserm/perceiver-io-optical-flow", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload OpticalFlowPerceiver
Browse files- config.json +54 -0
- pytorch_model.bin +3 -0
config.json
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{
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"architectures": [
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"OpticalFlowPerceiver"
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],
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"model_config": {
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"activation_checkpointing": false,
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"activation_offloading": false,
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"decoder": {
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"cross_attention_residual": false,
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"cross_attention_widening_factor": 1,
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"dropout": 0.1,
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"freeze": false,
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"image_shape": [
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368,
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496
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],
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"init_scale": 0.02,
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"num_cross_attention_heads": 1,
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"num_cross_attention_qk_channels": 512,
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"num_cross_attention_v_channels": 512,
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"rescale_factor": 100.0
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},
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"encoder": {
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"cross_attention_widening_factor": 1,
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"dropout": 0.1,
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"first_cross_attention_layer_shared": false,
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"first_self_attention_block_shared": true,
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"freeze": false,
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"image_shape": [
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368,
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496
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],
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"init_scale": 0.02,
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"num_cross_attention_heads": 1,
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"num_cross_attention_layers": 1,
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"num_cross_attention_qk_channels": null,
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"num_cross_attention_v_channels": null,
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"num_frequency_bands": 64,
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"num_patch_hidden_channels": 64,
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"num_patch_input_channels": 27,
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"num_self_attention_blocks": 1,
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"num_self_attention_heads": 16,
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"num_self_attention_layers_per_block": 24,
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"num_self_attention_qk_channels": null,
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"num_self_attention_v_channels": null,
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"self_attention_widening_factor": 1
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},
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"num_latent_channels": 512,
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"num_latents": 2048
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},
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"model_type": "perceiver-io-optical-flow",
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"torch_dtype": "float32",
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"transformers_version": "4.28.0"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:546875aa05244befa60a12a6986f4a1d58d5e306ea1418b8997a035c48b067ad
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size 352740341
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