End of training
Browse files- README.md +104 -196
- config.json +78 -0
- model.safetensors +3 -0
- runs/Jul29_14-22-32_d65c446d5ffa/events.out.tfevents.1722262958.d65c446d5ffa.24.0 +3 -0
- training_args.bin +3 -0
README.md
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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license: other
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base_model: nvidia/segformer-b2-finetuned-ade-512-512
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tags:
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- vision
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- image-segmentation
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- generated_from_trainer
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metrics:
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- precision
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model-index:
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- name: segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp
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results: []
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mouadn773/huggingface/runs/z400vwxm)
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# segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp
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This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b2-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0046
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- Mean Iou: 0.8880
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- Precision: 0.9115
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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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: 0.01
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- num_epochs: 40
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
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| 0.6668 | 0.9989 | 229 | 0.4009 | 0.5075 | 0.5321 |
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| 0.2583 | 1.9978 | 458 | 0.1436 | 0.6208 | 0.6535 |
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| 0.1355 | 2.9967 | 687 | 0.0809 | 0.7078 | 0.7644 |
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| 0.088 | 4.0 | 917 | 0.0585 | 0.7472 | 0.8136 |
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| 0.0638 | 4.9989 | 1146 | 0.0452 | 0.7737 | 0.8353 |
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| 0.05 | 5.9978 | 1375 | 0.0365 | 0.7845 | 0.8394 |
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| 0.0401 | 6.9967 | 1604 | 0.0344 | 0.8087 | 0.8717 |
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| 0.0332 | 8.0 | 1834 | 0.0277 | 0.8128 | 0.8682 |
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| 0.0286 | 8.9989 | 2063 | 0.0188 | 0.8210 | 0.8710 |
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| 0.0247 | 9.9978 | 2292 | 0.0148 | 0.8369 | 0.8881 |
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| 0.0214 | 10.9967 | 2521 | 0.0133 | 0.8332 | 0.8716 |
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| 0.0189 | 12.0 | 2751 | 0.0156 | 0.8286 | 0.8597 |
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| 0.017 | 12.9989 | 2980 | 0.0139 | 0.8397 | 0.8726 |
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| 0.0151 | 13.9978 | 3209 | 0.0154 | 0.8544 | 0.8943 |
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| 0.0139 | 14.9967 | 3438 | 0.0114 | 0.8553 | 0.8897 |
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| 0.0127 | 16.0 | 3668 | 0.0108 | 0.8517 | 0.8799 |
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| 0.0118 | 16.9989 | 3897 | 0.0075 | 0.8658 | 0.9040 |
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| 0.0108 | 17.9978 | 4126 | 0.0094 | 0.8700 | 0.9088 |
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| 0.0101 | 18.9967 | 4355 | 0.0084 | 0.8746 | 0.9151 |
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| 0.0094 | 20.0 | 4585 | 0.0071 | 0.8693 | 0.8973 |
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| 0.0088 | 20.9989 | 4814 | 0.0071 | 0.8668 | 0.8931 |
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| 0.0082 | 21.9978 | 5043 | 0.0060 | 0.8786 | 0.9151 |
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| 0.008 | 22.9967 | 5272 | 0.0063 | 0.8776 | 0.9109 |
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| 0.0075 | 24.0 | 5502 | 0.0066 | 0.8776 | 0.9052 |
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| 0.0071 | 24.9989 | 5731 | 0.0060 | 0.8807 | 0.9115 |
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| 0.0069 | 25.9978 | 5960 | 0.0062 | 0.8766 | 0.9004 |
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| 0.0065 | 26.9967 | 6189 | 0.0059 | 0.8754 | 0.8963 |
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| 0.0063 | 28.0 | 6419 | 0.0062 | 0.8825 | 0.9086 |
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| 0.006 | 28.9989 | 6648 | 0.0050 | 0.8839 | 0.9101 |
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| 0.0059 | 29.9978 | 6877 | 0.0051 | 0.8827 | 0.9069 |
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| 0.0057 | 30.9967 | 7106 | 0.0056 | 0.8822 | 0.9053 |
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| 0.0055 | 32.0 | 7336 | 0.0047 | 0.8866 | 0.9133 |
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| 0.0055 | 32.9989 | 7565 | 0.0046 | 0.8876 | 0.9135 |
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| 0.0053 | 33.9978 | 7794 | 0.0052 | 0.8839 | 0.9053 |
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| 0.0052 | 34.9967 | 8023 | 0.0048 | 0.8828 | 0.9035 |
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| 0.0051 | 36.0 | 8253 | 0.0046 | 0.8897 | 0.9156 |
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| 0.005 | 36.9989 | 8482 | 0.0045 | 0.8891 | 0.9137 |
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| 0.005 | 37.9978 | 8711 | 0.0047 | 0.8881 | 0.9120 |
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| 0.005 | 38.9967 | 8940 | 0.0047 | 0.8879 | 0.9110 |
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| 0.0049 | 39.9564 | 9160 | 0.0046 | 0.8880 | 0.9115 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.1.2
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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config.json
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{
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"_name_or_path": "nvidia/segformer-b2-finetuned-ade-512-512",
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"architectures": [
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"SegformerForSemanticSegmentation"
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],
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"attention_probs_dropout_prob": 0.0,
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"classifier_dropout_prob": 0.1,
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"decoder_hidden_size": 768,
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"depths": [
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3,
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],
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"downsampling_rates": [
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1,
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4,
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8,
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16
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],
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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_sizes": [
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64,
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128,
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320,
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512
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],
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"id2label": {
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"0": "unlabeled",
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"1": "PV"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"label2id": {
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"PV": 1,
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"unlabeled": 0
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},
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"layer_norm_eps": 1e-06,
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"mlp_ratios": [
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4,
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4,
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4,
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4
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],
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"model_type": "segformer",
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"num_attention_heads": [
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1,
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2,
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5,
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8
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],
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"num_channels": 3,
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"num_encoder_blocks": 4,
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"patch_sizes": [
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7,
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3,
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3,
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3
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],
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"reshape_last_stage": true,
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"semantic_loss_ignore_index": 255,
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"sr_ratios": [
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8,
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4,
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2,
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1
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],
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"strides": [
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4,
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2,
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2,
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2
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],
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"torch_dtype": "float32",
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"transformers_version": "4.42.3"
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}
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1 |
+
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training_args.bin
ADDED
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