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detr-resnet-50-finetuned-600-epochs-TW-Marine-5cls-dataset

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  1. README.md +52 -0
  2. checkpoint-144129/config.json +73 -0
  3. checkpoint-144129/model.safetensors +3 -0
  4. checkpoint-144129/optimizer.pt +3 -0
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  46. runs/Jul01_18-08-16_tzuchichen/events.out.tfevents.1751364497.tzuchichen.466.0 +3 -0
  47. runs/Jul01_18-08-31_tzuchichen/events.out.tfevents.1751364511.tzuchichen.637.0 +3 -0
  48. runs/Jul01_18-08-44_tzuchichen/events.out.tfevents.1751364525.tzuchichen.807.0 +3 -0
  49. runs/Jul01_18-09-00_tzuchichen/events.out.tfevents.1751364541.tzuchichen.1107.0 +3 -0
  50. runs/Jul01_18-09-23_tzuchichen/events.out.tfevents.1751364564.tzuchichen.1376.0 +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ base_model: ARG-NCTU/detr-resnet-50-finetuned-600-epochs-TW-Marine-2cls-dataset
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: detr-resnet-50-finetuned-600-epochs-TW-Marine-5cls-dataset
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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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+ # detr-resnet-50-finetuned-600-epochs-TW-Marine-5cls-dataset
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+
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+ This model is a fine-tuned version of [ARG-NCTU/detr-resnet-50-finetuned-600-epochs-TW-Marine-2cls-dataset](https://huggingface.co/ARG-NCTU/detr-resnet-50-finetuned-600-epochs-TW-Marine-2cls-dataset) on an unknown dataset.
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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: 1e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 450
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.3
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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+ Accumulating evaluation results...
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+ DONE (t=0.22s).
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+ IoU metric: bbox
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+ Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403
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+ Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.709
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+ Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375
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+ Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489
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+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.493
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+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.496
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+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
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+ Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.474
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+ Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.520
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+ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.617
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