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swin-tiny-patch4-window7-224-finetuned-geo-industrials

Image Classification
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Generated from Trainer
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  1. README.md +80 -0
  2. all_results.json +13 -0
  3. config.json +81 -0
  4. eval_results.json +8 -0
  5. model.safetensors +3 -0
  6. preprocessor_config.json +22 -0
  7. runs/Nov16_10-11-36_PC27LEL8/events.out.tfevents.1700147496.PC27LEL8 +3 -0
  8. runs/Nov16_10-11-36_PC27LEL8/events.out.tfevents.1700147884.PC27LEL8 +3 -0
  9. runs/Nov16_15-16-26_PC27LEL8/events.out.tfevents.1700165787.PC27LEL8 +3 -0
  10. runs/Nov16_15-16-26_PC27LEL8/events.out.tfevents.1700166077.PC27LEL8 +3 -0
  11. runs/Nov16_15-30-49_PC27LEL8/events.out.tfevents.1700166649.PC27LEL8 +3 -0
  12. runs/Nov16_15-30-49_PC27LEL8/events.out.tfevents.1700166964.PC27LEL8 +3 -0
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  24. runs/Nov17_14-47-12_PC27LEL8/events.out.tfevents.1700250432.PC27LEL8 +3 -0
  25. train_results.json +8 -0
  26. trainer_state.json +55 -0
  27. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/swin-tiny-patch4-window7-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: swin-tiny-patch4-window7-224-finetuned-geo-industrials
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9803921568627451
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # swin-tiny-patch4-window7-224-finetuned-geo-industrials
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+
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+ This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1426
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+ - Accuracy: 0.9804
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.8 | 3 | 0.1426 | 0.9804 |
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+ | No log | 1.87 | 7 | 0.0373 | 0.9804 |
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+ | No log | 2.4 | 9 | 0.0328 | 0.9804 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.0
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+ - Pytorch 2.1.0+cpu
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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+ {
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+ "epoch": 2.4,
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+ "eval_loss": 0.14262183010578156,
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+ "train_samples_per_second": 5.379,
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+ "train_steps_per_second": 0.035
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+ }
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+ {
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+ "_name_or_path": "microsoft/swin-tiny-patch4-window7-224",
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+ "architectures": [
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+ "SwinForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "depths": [
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "AnnualCrop",
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+ "1": "Cement",
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+ "2": "Forest",
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+ "3": "HerbaceousVegetation",
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+ "4": "Highway",
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+ "5": "Industrial",
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+ "6": "OilStorage",
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+ "7": "Pasture",
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+ "8": "PermanentCrop",
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+ "9": "Residential",
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+ "10": "River",
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+ "11": "SeaLake"
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+ },
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+ "initializer_range": 0.02,
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+ "layer_norm_eps": 1e-05,
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+ "mlp_ratio": 4.0,
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+ "model_type": "swin",
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+ "num_channels": 3,
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+ "num_heads": [
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+ "num_layers": 4,
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+ "out_features": [
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+ "out_indices": [
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+ "patch_size": 4,
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+ "path_norm": true,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "stage_names": [
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+ "stem",
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+ "stage1",
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+ "stage2",
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+ "stage3",
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+ "stage4"
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.0",
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+ "use_absolute_embeddings": false,
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+ "window_size": 7
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+ }
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