Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30") 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/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30") model = AutoModelForImageClassification.from_pretrained("ALM-AHME/beit-large-patch16-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- .gitignore +1 -0
- config.json +58 -0
- preprocessor_config.json +28 -0
- pytorch_model.bin +3 -0
- runs/Jul11_13-22-02_15fcd4b534d1/events.out.tfevents.1689081732.15fcd4b534d1.2772.0 +3 -0
- training_args.bin +3 -0
.gitignore
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checkpoint-*/
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config.json
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{
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"_name_or_path": "microsoft/beit-large-patch16-224",
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"architectures": [
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"BeitForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"auxiliary_channels": 256,
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"auxiliary_concat_input": false,
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"auxiliary_loss_weight": 0.4,
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"auxiliary_num_convs": 1,
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"drop_path_rate": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"id2label": {
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"0": "Lung-Benign_Tissue",
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"1": "Lung_Adenocarcinoma",
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"2": "Lung_Squamous_Cell_Carcinoma"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"Lung-Benign_Tissue": 0,
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"Lung_Adenocarcinoma": 1,
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"Lung_Squamous_Cell_Carcinoma": 2
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},
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"layer_norm_eps": 1e-12,
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"layer_scale_init_value": 0.1,
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"model_type": "beit",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 24,
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"out_indices": [
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3,
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5,
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7,
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11
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],
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"patch_size": 16,
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"pool_scales": [
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1,
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2,
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3,
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6
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],
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"problem_type": "single_label_classification",
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"semantic_loss_ignore_index": 255,
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"torch_dtype": "float32",
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"transformers_version": "4.30.2",
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"use_absolute_position_embeddings": false,
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"use_auxiliary_head": true,
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"use_mask_token": false,
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"use_mean_pooling": true,
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"use_relative_position_bias": true,
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"use_shared_relative_position_bias": false,
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"vocab_size": 8192
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}
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preprocessor_config.json
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{
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"crop_size": {
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"height": 224,
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"width": 224
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},
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"do_center_crop": false,
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"do_normalize": true,
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"do_reduce_labels": false,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_processor_type": "BeitImageProcessor",
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"image_std": [
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0.5,
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0.5,
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0.5
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],
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 224,
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"width": 224
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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:d783fc572a284c140dfedcc47014104602630262e0772fd6a166d66a01caae26
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size 1221245577
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runs/Jul11_13-22-02_15fcd4b534d1/events.out.tfevents.1689081732.15fcd4b534d1.2772.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:fa852c9ce8c2ba0b97220a79ef9bc29b8566a9ac34007a0ae7680530dd327fba
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size 7984
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:75997ea7324a8a83fc972c8b935a31cf12dfcc225596631773f462d385f7f792
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size 4091
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