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Browse files- OUTPUTS/cifnet-18-tiny-lr0.01-attention/all_results.json +1 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/config.json +67 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/1712011059.4757543/events.out.tfevents.1712011059.ids-ws-06.3573944.1 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/1712011059.478225/hparams.yml +30 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/accuracy_accuracy/events.out.tfevents.1712011313.ids-ws-06.3573944.2 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/events.out.tfevents.1712011059.ids-ws-06.3573944.0 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/model.safetensors +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-attention/preprocessor_config.json +37 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/all_results.json +1 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/config.json +60 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/image_classification_no_trainer/1712011004.1205187/events.out.tfevents.1712011004.ids-ws-06.3571203.1 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/image_classification_no_trainer/1712011004.1220295/hparams.yml +30 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/image_classification_no_trainer/accuracy_accuracy/events.out.tfevents.1712011108.ids-ws-06.3571203.2 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/image_classification_no_trainer/events.out.tfevents.1712011004.ids-ws-06.3571203.0 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/model.safetensors +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/preprocessor_config.json +37 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/all_results.json +1 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/config.json +58 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/1711990816.257815/events.out.tfevents.1711990816.ids-ws-06.2843209.1 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/1711990816.2593696/hparams.yml +30 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/accuracy_accuracy/events.out.tfevents.1711990897.ids-ws-06.2843209.2 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/events.out.tfevents.1711990816.ids-ws-06.2843209.0 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/events.out.tfevents.1712005878.ids-ws-06.3270132.0 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/events.out.tfevents.1712005910.ids-ws-06.3270878.0 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/image_classification_no_trainer/events.out.tfevents.1712005974.ids-ws-06.3271814.0 +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/model.safetensors +3 -0
- OUTPUTS/cifnet-18-tiny-lr0.1-baseline/preprocessor_config.json +37 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/all_results.json +1 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/config.json +67 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/1712166496.7234852/events.out.tfevents.1712166496.ids-ws-06.1309582.1 +3 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/1712166496.724743/hparams.yml +30 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/accuracy_accuracy/events.out.tfevents.1712166664.ids-ws-06.1309582.2 +3 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/events.out.tfevents.1712166496.ids-ws-06.1309582.0 +3 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/model.safetensors +3 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/preprocessor_config.json +37 -0
- OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/test_model.log +244 -0
OUTPUTS/cifnet-18-tiny-lr0.01-attention/all_results.json
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{"eval_accuracy": 0.5882666666666667}
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/config.json
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/1712011059.4757543/events.out.tfevents.1712011059.ids-ws-06.3573944.1
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/1712011059.478225/hparams.yml
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checkpointing_steps: null
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dataset_name: cifar10
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ignore_mismatched_sizes: false
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image_column_name: img
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max_eval_samples: null
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max_train_steps: 64000
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model_name_or_path: MODELS/cifnet-18-tiny_attention
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num_train_epochs: 193
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num_warmup_steps: 6400
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num_workers: 32
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output_dir: OUTPUTS/cifnet-18-tiny-lr0.01-attention
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per_device_eval_batch_size: 8
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per_device_train_batch_size: 128
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report_to: tensorboard
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/accuracy_accuracy/events.out.tfevents.1712011313.ids-ws-06.3573944.2
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/image_classification_no_trainer/events.out.tfevents.1712011059.ids-ws-06.3573944.0
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/model.safetensors
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OUTPUTS/cifnet-18-tiny-lr0.01-attention/preprocessor_config.json
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OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/all_results.json
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OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/config.json
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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| 28 |
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| 34 |
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|
| 36 |
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|
| 37 |
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|
OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/all_results.json
ADDED
|
@@ -0,0 +1 @@
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| 1 |
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{"eval_accuracy": 0.8581333333333333}
|
OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/config.json
ADDED
|
@@ -0,0 +1,67 @@
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|
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|
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|
|
|
|
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|
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|
|
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|
|
|
|
|
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|
|
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|
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|
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|
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|
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|
| 1 |
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{
|
| 2 |
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"_name_or_path": "microsoft/resnet-18",
|
| 3 |
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"activation": "silu",
|
| 4 |
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"architectures": [
|
| 5 |
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"CifNetForImageClassification"
|
| 6 |
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],
|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
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|
| 15 |
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|
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
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| 21 |
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|
| 24 |
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|
| 26 |
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"embedding_size": 64,
|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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"hidden_sizes": [
|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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"id2label": {
|
| 37 |
+
"0": "airplane",
|
| 38 |
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"1": "automobile",
|
| 39 |
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"2": "bird",
|
| 40 |
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"3": "cat",
|
| 41 |
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"4": "deer",
|
| 42 |
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"5": "dog",
|
| 43 |
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"6": "frog",
|
| 44 |
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"7": "horse",
|
| 45 |
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"8": "ship",
|
| 46 |
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"9": "truck"
|
| 47 |
+
},
|
| 48 |
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"label2id": {
|
| 49 |
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"airplane": "0",
|
| 50 |
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"automobile": "1",
|
| 51 |
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"bird": "2",
|
| 52 |
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"cat": "3",
|
| 53 |
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"deer": "4",
|
| 54 |
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"dog": "5",
|
| 55 |
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"frog": "6",
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| 56 |
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"horse": "7",
|
| 57 |
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"ship": "8",
|
| 58 |
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"truck": "9"
|
| 59 |
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},
|
| 60 |
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"layer_type": "attention",
|
| 61 |
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"main_kernel_size": 3,
|
| 62 |
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"model_type": "resnet",
|
| 63 |
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"num_channels": 3,
|
| 64 |
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"problem_type": "single_label_classification",
|
| 65 |
+
"torch_dtype": "float32",
|
| 66 |
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"transformers_version": "4.39.2"
|
| 67 |
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}
|
OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/1712166496.7234852/events.out.tfevents.1712166496.ids-ws-06.1309582.1
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size 1538
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OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/1712166496.724743/hparams.yml
ADDED
|
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checkpointing_steps: null
|
| 2 |
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dataset_name: cifar10
|
| 3 |
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gradient_accumulation_steps: 1
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| 4 |
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hub_model_id: null
|
| 5 |
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hub_token: null
|
| 6 |
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ignore_mismatched_sizes: false
|
| 7 |
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image_column_name: img
|
| 8 |
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label_column_name: label
|
| 9 |
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learning_rate: 0.001
|
| 10 |
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lr_scheduler_type: cosine
|
| 11 |
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max_eval_samples: null
|
| 12 |
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max_train_samples: null
|
| 13 |
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max_train_steps: 64000
|
| 14 |
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model_name_or_path: MODELS/cifnet-18-tiny_attention
|
| 15 |
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num_train_epochs: 193
|
| 16 |
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num_warmup_steps: 6400
|
| 17 |
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num_workers: 32
|
| 18 |
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output_dir: OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm
|
| 19 |
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per_device_eval_batch_size: 8
|
| 20 |
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per_device_train_batch_size: 128
|
| 21 |
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push_to_hub: false
|
| 22 |
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report_to: tensorboard
|
| 23 |
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resume_from_checkpoint: null
|
| 24 |
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seed: 42
|
| 25 |
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train_dir: null
|
| 26 |
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train_val_split: 0.15
|
| 27 |
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trust_remote_code: false
|
| 28 |
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validation_dir: null
|
| 29 |
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weight_decay: 0.0
|
| 30 |
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with_tracking: true
|
OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/accuracy_accuracy/events.out.tfevents.1712166664.ids-ws-06.1309582.2
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size 7097748
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OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/model.safetensors
ADDED
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OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/preprocessor_config.json
ADDED
|
@@ -0,0 +1,37 @@
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| 1 |
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{
|
| 2 |
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"_valid_processor_keys": [
|
| 3 |
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|
| 4 |
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|
| 5 |
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"size",
|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
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|
| 14 |
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|
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|
| 16 |
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|
| 37 |
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|
OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/test_model.log
ADDED
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|
|
|
| 1 |
+
CifNetForImageClassification(
|
| 2 |
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(resnet): CifNetModel(
|
| 3 |
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(embedder): CifNetEmbeddings(
|
| 4 |
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(embedder): CifNetConvLayer(
|
| 5 |
+
(convolution): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
|
| 6 |
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(normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 7 |
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(activation): SiLU()
|
| 8 |
+
)
|
| 9 |
+
(pooler): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 10 |
+
)
|
| 11 |
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(encoder): CifNetEncoder(
|
| 12 |
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(stages): ModuleList(
|
| 13 |
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(0): CifNetStage(
|
| 14 |
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(layers): Sequential(
|
| 15 |
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(0): CifNetSelfAttentionLayer(
|
| 16 |
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(shortcut): CifNetShortCut(
|
| 17 |
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(convolution): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
| 18 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 19 |
+
)
|
| 20 |
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(in_conv): CifNetConvLayer(
|
| 21 |
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(convolution): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
| 22 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 23 |
+
(activation): SiLU()
|
| 24 |
+
)
|
| 25 |
+
(attention): CifNetSelfAttention(
|
| 26 |
+
(q_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 27 |
+
(k_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 28 |
+
(v_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 29 |
+
(o_proj): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
|
| 30 |
+
)
|
| 31 |
+
(activation): SiLU()
|
| 32 |
+
(attention_norm): CifNetRMSNorm()
|
| 33 |
+
(out_conv): CifNetConvLayer(
|
| 34 |
+
(convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 35 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 36 |
+
(activation): SiLU()
|
| 37 |
+
)
|
| 38 |
+
)
|
| 39 |
+
)
|
| 40 |
+
)
|
| 41 |
+
(1-3): 3 x CifNetStage(
|
| 42 |
+
(layers): Sequential(
|
| 43 |
+
(0): CifNetSelfAttentionLayer(
|
| 44 |
+
(shortcut): Identity()
|
| 45 |
+
(in_conv): CifNetConvLayer(
|
| 46 |
+
(convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 47 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 48 |
+
(activation): SiLU()
|
| 49 |
+
)
|
| 50 |
+
(attention): CifNetSelfAttention(
|
| 51 |
+
(q_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 52 |
+
(k_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 53 |
+
(v_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 54 |
+
(o_proj): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
|
| 55 |
+
)
|
| 56 |
+
(activation): SiLU()
|
| 57 |
+
(attention_norm): CifNetRMSNorm()
|
| 58 |
+
(out_conv): CifNetConvLayer(
|
| 59 |
+
(convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 60 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 61 |
+
(activation): SiLU()
|
| 62 |
+
)
|
| 63 |
+
)
|
| 64 |
+
(1): CifNetSelfAttentionLayer(
|
| 65 |
+
(shortcut): Identity()
|
| 66 |
+
(in_conv): CifNetConvLayer(
|
| 67 |
+
(convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 68 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 69 |
+
(activation): SiLU()
|
| 70 |
+
)
|
| 71 |
+
(attention): CifNetSelfAttention(
|
| 72 |
+
(q_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 73 |
+
(k_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 74 |
+
(v_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
|
| 75 |
+
(o_proj): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
|
| 76 |
+
)
|
| 77 |
+
(activation): SiLU()
|
| 78 |
+
(attention_norm): CifNetRMSNorm()
|
| 79 |
+
(out_conv): CifNetConvLayer(
|
| 80 |
+
(convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 81 |
+
(normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 82 |
+
(activation): SiLU()
|
| 83 |
+
)
|
| 84 |
+
)
|
| 85 |
+
)
|
| 86 |
+
)
|
| 87 |
+
)
|
| 88 |
+
)
|
| 89 |
+
(pooler): AdaptiveAvgPool2d(output_size=(1, 1))
|
| 90 |
+
)
|
| 91 |
+
(classifier): Sequential(
|
| 92 |
+
(0): Flatten(start_dim=1, end_dim=-1)
|
| 93 |
+
(1): Linear(in_features=128, out_features=10, bias=True)
|
| 94 |
+
)
|
| 95 |
+
)
|
| 96 |
+
----------------------------------------------------------------
|
| 97 |
+
Layer (type) Output Shape Param #
|
| 98 |
+
================================================================
|
| 99 |
+
Conv2d-1 [4, 64, 112, 112] 9,408
|
| 100 |
+
BatchNorm2d-2 [4, 64, 112, 112] 128
|
| 101 |
+
SiLU-3 [4, 64, 112, 112] 0
|
| 102 |
+
CifNetConvLayer-4 [4, 64, 112, 112] 0
|
| 103 |
+
MaxPool2d-5 [4, 64, 56, 56] 0
|
| 104 |
+
CifNetEmbeddings-6 [4, 64, 56, 56] 0
|
| 105 |
+
Conv2d-7 [4, 128, 28, 28] 73,728
|
| 106 |
+
BatchNorm2d-8 [4, 128, 28, 28] 256
|
| 107 |
+
SiLU-9 [4, 128, 28, 28] 0
|
| 108 |
+
CifNetConvLayer-10 [4, 128, 28, 28] 0
|
| 109 |
+
CifNetRMSNorm-11 [4, 28, 28, 128] 128
|
| 110 |
+
Conv2d-12 [4, 32, 28, 28] 4,128
|
| 111 |
+
Conv2d-13 [4, 32, 28, 28] 4,128
|
| 112 |
+
Conv2d-14 [4, 32, 28, 28] 4,128
|
| 113 |
+
Conv2d-15 [4, 128, 28, 28] 4,224
|
| 114 |
+
CifNetSelfAttention-16 [4, 128, 28, 28] 0
|
| 115 |
+
SiLU-17 [4, 128, 28, 28] 0
|
| 116 |
+
Conv2d-18 [4, 128, 28, 28] 147,456
|
| 117 |
+
BatchNorm2d-19 [4, 128, 28, 28] 256
|
| 118 |
+
SiLU-20 [4, 128, 28, 28] 0
|
| 119 |
+
CifNetConvLayer-21 [4, 128, 28, 28] 0
|
| 120 |
+
Conv2d-22 [4, 128, 28, 28] 8,192
|
| 121 |
+
BatchNorm2d-23 [4, 128, 28, 28] 256
|
| 122 |
+
CifNetShortCut-24 [4, 128, 28, 28] 0
|
| 123 |
+
CifNetSelfAttentionLayer-25 [4, 128, 28, 28] 0
|
| 124 |
+
CifNetStage-26 [4, 128, 28, 28] 0
|
| 125 |
+
Conv2d-27 [4, 128, 28, 28] 147,456
|
| 126 |
+
BatchNorm2d-28 [4, 128, 28, 28] 256
|
| 127 |
+
SiLU-29 [4, 128, 28, 28] 0
|
| 128 |
+
CifNetConvLayer-30 [4, 128, 28, 28] 0
|
| 129 |
+
CifNetRMSNorm-31 [4, 28, 28, 128] 128
|
| 130 |
+
Conv2d-32 [4, 32, 28, 28] 4,128
|
| 131 |
+
Conv2d-33 [4, 32, 28, 28] 4,128
|
| 132 |
+
Conv2d-34 [4, 32, 28, 28] 4,128
|
| 133 |
+
Conv2d-35 [4, 128, 28, 28] 4,224
|
| 134 |
+
CifNetSelfAttention-36 [4, 128, 28, 28] 0
|
| 135 |
+
SiLU-37 [4, 128, 28, 28] 0
|
| 136 |
+
Conv2d-38 [4, 128, 28, 28] 147,456
|
| 137 |
+
BatchNorm2d-39 [4, 128, 28, 28] 256
|
| 138 |
+
SiLU-40 [4, 128, 28, 28] 0
|
| 139 |
+
CifNetConvLayer-41 [4, 128, 28, 28] 0
|
| 140 |
+
Identity-42 [4, 128, 28, 28] 0
|
| 141 |
+
CifNetSelfAttentionLayer-43 [4, 128, 28, 28] 0
|
| 142 |
+
Conv2d-44 [4, 128, 28, 28] 147,456
|
| 143 |
+
BatchNorm2d-45 [4, 128, 28, 28] 256
|
| 144 |
+
SiLU-46 [4, 128, 28, 28] 0
|
| 145 |
+
CifNetConvLayer-47 [4, 128, 28, 28] 0
|
| 146 |
+
CifNetRMSNorm-48 [4, 28, 28, 128] 128
|
| 147 |
+
Conv2d-49 [4, 32, 28, 28] 4,128
|
| 148 |
+
Conv2d-50 [4, 32, 28, 28] 4,128
|
| 149 |
+
Conv2d-51 [4, 32, 28, 28] 4,128
|
| 150 |
+
Conv2d-52 [4, 128, 28, 28] 4,224
|
| 151 |
+
CifNetSelfAttention-53 [4, 128, 28, 28] 0
|
| 152 |
+
SiLU-54 [4, 128, 28, 28] 0
|
| 153 |
+
Conv2d-55 [4, 128, 28, 28] 147,456
|
| 154 |
+
BatchNorm2d-56 [4, 128, 28, 28] 256
|
| 155 |
+
SiLU-57 [4, 128, 28, 28] 0
|
| 156 |
+
CifNetConvLayer-58 [4, 128, 28, 28] 0
|
| 157 |
+
Identity-59 [4, 128, 28, 28] 0
|
| 158 |
+
CifNetSelfAttentionLayer-60 [4, 128, 28, 28] 0
|
| 159 |
+
CifNetStage-61 [4, 128, 28, 28] 0
|
| 160 |
+
Conv2d-62 [4, 128, 28, 28] 147,456
|
| 161 |
+
BatchNorm2d-63 [4, 128, 28, 28] 256
|
| 162 |
+
SiLU-64 [4, 128, 28, 28] 0
|
| 163 |
+
CifNetConvLayer-65 [4, 128, 28, 28] 0
|
| 164 |
+
CifNetRMSNorm-66 [4, 28, 28, 128] 128
|
| 165 |
+
Conv2d-67 [4, 32, 28, 28] 4,128
|
| 166 |
+
Conv2d-68 [4, 32, 28, 28] 4,128
|
| 167 |
+
Conv2d-69 [4, 32, 28, 28] 4,128
|
| 168 |
+
Conv2d-70 [4, 128, 28, 28] 4,224
|
| 169 |
+
CifNetSelfAttention-71 [4, 128, 28, 28] 0
|
| 170 |
+
SiLU-72 [4, 128, 28, 28] 0
|
| 171 |
+
Conv2d-73 [4, 128, 28, 28] 147,456
|
| 172 |
+
BatchNorm2d-74 [4, 128, 28, 28] 256
|
| 173 |
+
SiLU-75 [4, 128, 28, 28] 0
|
| 174 |
+
CifNetConvLayer-76 [4, 128, 28, 28] 0
|
| 175 |
+
Identity-77 [4, 128, 28, 28] 0
|
| 176 |
+
CifNetSelfAttentionLayer-78 [4, 128, 28, 28] 0
|
| 177 |
+
Conv2d-79 [4, 128, 28, 28] 147,456
|
| 178 |
+
BatchNorm2d-80 [4, 128, 28, 28] 256
|
| 179 |
+
SiLU-81 [4, 128, 28, 28] 0
|
| 180 |
+
CifNetConvLayer-82 [4, 128, 28, 28] 0
|
| 181 |
+
CifNetRMSNorm-83 [4, 28, 28, 128] 128
|
| 182 |
+
Conv2d-84 [4, 32, 28, 28] 4,128
|
| 183 |
+
Conv2d-85 [4, 32, 28, 28] 4,128
|
| 184 |
+
Conv2d-86 [4, 32, 28, 28] 4,128
|
| 185 |
+
Conv2d-87 [4, 128, 28, 28] 4,224
|
| 186 |
+
CifNetSelfAttention-88 [4, 128, 28, 28] 0
|
| 187 |
+
SiLU-89 [4, 128, 28, 28] 0
|
| 188 |
+
Conv2d-90 [4, 128, 28, 28] 147,456
|
| 189 |
+
BatchNorm2d-91 [4, 128, 28, 28] 256
|
| 190 |
+
SiLU-92 [4, 128, 28, 28] 0
|
| 191 |
+
CifNetConvLayer-93 [4, 128, 28, 28] 0
|
| 192 |
+
Identity-94 [4, 128, 28, 28] 0
|
| 193 |
+
CifNetSelfAttentionLayer-95 [4, 128, 28, 28] 0
|
| 194 |
+
CifNetStage-96 [4, 128, 28, 28] 0
|
| 195 |
+
Conv2d-97 [4, 128, 28, 28] 147,456
|
| 196 |
+
BatchNorm2d-98 [4, 128, 28, 28] 256
|
| 197 |
+
SiLU-99 [4, 128, 28, 28] 0
|
| 198 |
+
CifNetConvLayer-100 [4, 128, 28, 28] 0
|
| 199 |
+
CifNetRMSNorm-101 [4, 28, 28, 128] 128
|
| 200 |
+
Conv2d-102 [4, 32, 28, 28] 4,128
|
| 201 |
+
Conv2d-103 [4, 32, 28, 28] 4,128
|
| 202 |
+
Conv2d-104 [4, 32, 28, 28] 4,128
|
| 203 |
+
Conv2d-105 [4, 128, 28, 28] 4,224
|
| 204 |
+
CifNetSelfAttention-106 [4, 128, 28, 28] 0
|
| 205 |
+
SiLU-107 [4, 128, 28, 28] 0
|
| 206 |
+
Conv2d-108 [4, 128, 28, 28] 147,456
|
| 207 |
+
BatchNorm2d-109 [4, 128, 28, 28] 256
|
| 208 |
+
SiLU-110 [4, 128, 28, 28] 0
|
| 209 |
+
CifNetConvLayer-111 [4, 128, 28, 28] 0
|
| 210 |
+
Identity-112 [4, 128, 28, 28] 0
|
| 211 |
+
CifNetSelfAttentionLayer-113 [4, 128, 28, 28] 0
|
| 212 |
+
Conv2d-114 [4, 128, 28, 28] 147,456
|
| 213 |
+
BatchNorm2d-115 [4, 128, 28, 28] 256
|
| 214 |
+
SiLU-116 [4, 128, 28, 28] 0
|
| 215 |
+
CifNetConvLayer-117 [4, 128, 28, 28] 0
|
| 216 |
+
CifNetRMSNorm-118 [4, 28, 28, 128] 128
|
| 217 |
+
Conv2d-119 [4, 32, 28, 28] 4,128
|
| 218 |
+
Conv2d-120 [4, 32, 28, 28] 4,128
|
| 219 |
+
Conv2d-121 [4, 32, 28, 28] 4,128
|
| 220 |
+
Conv2d-122 [4, 128, 28, 28] 4,224
|
| 221 |
+
CifNetSelfAttention-123 [4, 128, 28, 28] 0
|
| 222 |
+
SiLU-124 [4, 128, 28, 28] 0
|
| 223 |
+
Conv2d-125 [4, 128, 28, 28] 147,456
|
| 224 |
+
BatchNorm2d-126 [4, 128, 28, 28] 256
|
| 225 |
+
SiLU-127 [4, 128, 28, 28] 0
|
| 226 |
+
CifNetConvLayer-128 [4, 128, 28, 28] 0
|
| 227 |
+
Identity-129 [4, 128, 28, 28] 0
|
| 228 |
+
CifNetSelfAttentionLayer-130 [4, 128, 28, 28] 0
|
| 229 |
+
CifNetStage-131 [4, 128, 28, 28] 0
|
| 230 |
+
CifNetEncoder-132 [[-1, 128, 28, 28]] 0
|
| 231 |
+
AdaptiveAvgPool2d-133 [4, 128, 1, 1] 0
|
| 232 |
+
CifNetModel-134 [[-1, 128, 28, 28], [-1, 128, 1, 1]] 0
|
| 233 |
+
Flatten-135 [4, 128] 0
|
| 234 |
+
Linear-136 [4, 10] 1,290
|
| 235 |
+
================================================================
|
| 236 |
+
Total params: 2,130,666
|
| 237 |
+
Trainable params: 2,130,666
|
| 238 |
+
Non-trainable params: 0
|
| 239 |
+
----------------------------------------------------------------
|
| 240 |
+
Input size (MB): 2.30
|
| 241 |
+
Forward/backward pass size (MB): 542.07
|
| 242 |
+
Params size (MB): 8.13
|
| 243 |
+
Estimated Total Size (MB): 552.50
|
| 244 |
+
----------------------------------------------------------------
|