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mobilevit-trained-task3

Image Classification
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mobilevitv2
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paolinox/mobilevit-Trained-EUROSAT-Multispectral

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  1. README.md +82 -0
  2. config.json +67 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +16 -0
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README.md ADDED
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+ ---
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+ license: other
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+ base_model: apple/mobilevitv2-1.0-imagenet1k-256
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: mobilevit-trained-task3
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+ results: []
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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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+ # mobilevit-trained-task3
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+
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+ This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1371
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+ - Accuracy: 0.9670
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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: 0.001
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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: 20
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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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+ | 0.6753 | 0.99 | 126 | 0.8382 | 0.7376 |
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+ | 0.6882 | 2.0 | 253 | 0.6129 | 0.7874 |
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+ | 0.4068 | 3.0 | 380 | 0.3532 | 0.8876 |
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+ | 0.3587 | 4.0 | 507 | 0.4896 | 0.8622 |
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+ | 0.3013 | 4.99 | 633 | 0.2656 | 0.9078 |
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+ | 0.2777 | 6.0 | 760 | 0.1679 | 0.9472 |
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+ | 0.2093 | 7.0 | 887 | 0.2264 | 0.9302 |
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+ | 0.1866 | 8.0 | 1014 | 0.2245 | 0.9263 |
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+ | 0.1896 | 8.99 | 1140 | 0.2252 | 0.9333 |
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+ | 0.1059 | 10.0 | 1267 | 0.1544 | 0.9528 |
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+ | 0.1072 | 11.0 | 1394 | 0.2232 | 0.9391 |
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+ | 0.1121 | 12.0 | 1521 | 0.1723 | 0.9467 |
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+ | 0.103 | 12.99 | 1647 | 0.1750 | 0.9530 |
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+ | 0.071 | 14.0 | 1774 | 0.1713 | 0.9541 |
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+ | 0.0276 | 15.0 | 1901 | 0.1384 | 0.9631 |
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+ | 0.0279 | 16.0 | 2028 | 0.1575 | 0.9607 |
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+ | 0.0396 | 16.99 | 2154 | 0.1579 | 0.9604 |
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+ | 0.0129 | 18.0 | 2281 | 0.1389 | 0.9674 |
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+ | 0.0031 | 19.0 | 2408 | 0.1315 | 0.9689 |
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+ | 0.0074 | 19.88 | 2520 | 0.1371 | 0.9670 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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+ {
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+ "_name_or_path": "apple/mobilevitv2-1.0-imagenet1k-256",
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+ "architectures": [
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+ "MobileViTV2ForImageClassification"
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+ ],
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+ "aspp_dropout_prob": 0.1,
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+ "aspp_out_channels": 512,
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+ "atrous_rates": [
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+ "attn_dropout": 0.0,
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+ "base_attn_unit_dims": [
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+ 128,
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+ 256
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+ ],
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+ "classifier_dropout_prob": 0.1,
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+ "conv_kernel_size": 3,
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+ "expand_ratio": 2.0,
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+ "ffn_dropout": 0.0,
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+ "ffn_multiplier": 2,
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+ "hidden_act": "swish",
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+ "id2label": {
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+ "0": "Forest",
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+ "1": "AnnualCrop",
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+ "2": "Highway",
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+ "3": "PermanentCrop",
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+ "4": "Industrial",
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+ "5": "River",
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+ "6": "SeaLake",
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+ "7": "HerbaceousVegetation",
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+ "8": "Pasture",
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+ "9": "Residential"
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+ },
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+ "image_size": 64,
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "AnnualCrop": 1,
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+ "Forest": 0,
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+ "PermanentCrop": 3,
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+ "Residential": 9,
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+ "River": 5,
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+ "SeaLake": 6
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "mlp_ratio": 2.0,
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+ "model_type": "mobilevitv2",
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+ "n_attn_blocks": [
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+ 4,
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+ 3
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+ ],
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+ "num_channels": 13,
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+ "output_stride": 32,
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+ "patch_size": 2,
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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": "4.37.2",
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+ "width_multiplier": 1.0
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+ }
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