Image Classification
Transformers
Safetensors
mobilevit
knowledge_distillation
vision
Generated from Trainer
Instructions to use c14kevincardenas/mobilevit-x-small_alpha0.7_temp3.0_t3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use c14kevincardenas/mobilevit-x-small_alpha0.7_temp3.0_t3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="c14kevincardenas/mobilevit-x-small_alpha0.7_temp3.0_t3") 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("c14kevincardenas/mobilevit-x-small_alpha0.7_temp3.0_t3") model = AutoModelForImageClassification.from_pretrained("c14kevincardenas/mobilevit-x-small_alpha0.7_temp3.0_t3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 773 Bytes
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"architectures": [
"MobileViTForImageClassification"
],
"aspp_dropout_prob": 0.1,
"aspp_out_channels": 256,
"atrous_rates": [
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"attention_probs_dropout_prob": 0.0,
"classifier_dropout_prob": 0.1,
"conv_kernel_size": 3,
"expand_ratio": 4.0,
"hidden_act": "silu",
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"hidden_sizes": [
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],
"layer_norm_eps": 1e-05,
"mlp_ratio": 2.0,
"model_type": "mobilevit",
"neck_hidden_sizes": [
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],
"num_attention_heads": 4,
"num_channels": 3,
"output_stride": 32,
"patch_size": 2,
"qkv_bias": true,
"semantic_loss_ignore_index": 255,
"torch_dtype": "float32",
"transformers_version": "4.20.0.dev0"
} |