Instructions to use ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3") 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("ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3") model = AutoModelForImageClassification.from_pretrained("ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- config.json +64 -0
- preprocessor_config.json +22 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
config.json
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{
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"_name_or_path": "microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft",
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"architectures": [
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"Swinv2ForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"depths": [
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2,
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2,
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18,
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2
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],
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"drop_path_rate": 0.1,
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"embed_dim": 192,
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"encoder_stride": 32,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1536,
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"id2label": {
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"0": "akiec",
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"1": "bcc",
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"2": "bkl",
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"3": "df",
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"4": "mel",
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"5": "nv",
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"6": "vasc"
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},
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"image_size": 256,
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"initializer_range": 0.02,
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"label2id": {
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"akiec": 0,
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"bcc": 1,
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"bkl": 2,
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"df": 3,
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"mel": 4,
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"nv": 5,
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"vasc": 6
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},
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"layer_norm_eps": 1e-05,
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"mlp_ratio": 4.0,
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"model_type": "swinv2",
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"num_channels": 3,
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"num_heads": [
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6,
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12,
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24,
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48
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],
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"num_layers": 4,
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"patch_size": 4,
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"path_norm": true,
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"pretrained_window_sizes": [
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0,
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0,
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0,
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0
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],
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.32.0",
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"use_absolute_embeddings": false,
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"window_size": 16
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ViTImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 256,
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"width": 256
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}
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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 sha256:7204a5fb6c44ad8bf5144e0cbb6f4a6c7855dcf2b1758bafd5d317b184c56f1c
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size 781022769
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c8a29588348cdb9a67378368bc1fab05ada7473ba026fd8bcaf63cea11b47927
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size 4219
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