e2am: batch upload 13 files (374919 KB)
Browse files- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/best_model.pt +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/checkpoints/checkpoint_last.pt +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/config.yaml +32 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/accuracy_curve.png +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/accuracy_vs_energy.png +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/cumulative_energy.png +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/epoch_energy.png +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/gpu_power_trace.png +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/loss_curve.png +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/final_model.pt +3 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/gpu_power_trace.csv +0 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/history.csv +2 -0
- runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/metrics_summary.json +27 -0
runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/best_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:c7171b22edde776c445995af316dbf140c4610fb423ef68ebd19df9d0d9a4119
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size 95965667
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/checkpoints/checkpoint_last.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:abdc62604e0354fa282b4b20cfa2c5aa9a0298b88ae8fa9c028c82c4c153518c
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size 191653610
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/config.yaml
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project_name: E2AM-ResNet50
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model_name: resnet50_smallstem
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dataset_name: tiny_imagenet
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output_root: /kaggle/working/E2AM-ResNet50
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data_root: /kaggle/working/tiny-imagenet-200
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seed: 42
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num_epochs: 60
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batch_size: 96
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image_size: 64
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optimizer: sgd
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learning_rate: 0.1
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momentum: 0.9
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weight_decay: 0.0005
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warmup_epochs: 2
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amp_enabled: false
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cache_dataset: true
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gradient_accumulation_steps: 1
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l1_lambda: 0.0
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scheduler: cosine
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eag_enabled: false
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eag_threshold: 1.0e-07
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eag_patience: 5
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energy_sample_interval_sec: 1.0
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carbon_intensity_kg_per_kwh: 0.475
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milestone_push_every_epochs: 10
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heavy_push_every_epochs: 10
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force_rerun: false
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cleanup_local_after_complete: true
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cleanup_keep_summary: false
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method_group: individual_methods
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variant_name: M5_adaptive_lr_only
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method_id: M5
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/accuracy_curve.png
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Git LFS Details
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/accuracy_vs_energy.png
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Git LFS Details
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/cumulative_energy.png
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Git LFS Details
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/epoch_energy.png
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Git LFS Details
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/gpu_power_trace.png
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Git LFS Details
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/figures/loss_curve.png
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Git LFS Details
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/final_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee29133328fc5e73643ecac29c8898522a68b4d7cf61493de9e9e0a5bace2b5a
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size 95965993
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/gpu_power_trace.csv
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/history.csv
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epoch,train_loss,val_loss,train_accuracy,val_accuracy,val_accuracy_top5,f1_score,precision,recall,learning_rate,batch_size,effective_batch_size,amp_enabled,l1_lambda,gradient_accumulation_steps,epoch_time_sec,cumulative_time_sec,epoch_energy_j,cumulative_energy_j,epoch_co2_kg,cumulative_co2_kg,peak_vram_mb,eag_score,early_stop_counter,timestamp_utc
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0,5.164085664215088,4.825000302124024,0.01771,0.0367,0.1303,0.018325724701464197,0.017604986127719564,0.0367,0.05,96,96,False,0.0,1,1325.2561547756195,1325.2561547756195,99477.21524751316,99477.21524751316,0.013125465900713541,0.013125465900713541,12862.6044921875,0.0,0,2026-05-15T02:20:47Z
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runs/tiny_imagenet/individual_methods/M5_adaptive_lr_only/metrics_summary.json
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{
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"run_key": "resnet50_smallstem::tiny_imagenet::individual_methods::M5_adaptive_lr_only",
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"run_dir": "/kaggle/working/E2AM-ResNet50/tiny_imagenet/individual_methods/M5_adaptive_lr_only",
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"model_name": "resnet50_smallstem",
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"dataset_name": "tiny_imagenet",
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"method_group": "individual_methods",
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"variant_name": "M5_adaptive_lr_only",
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"method_id": "M5",
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"num_epochs_planned": 60,
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"num_epochs_run_so_far": 1,
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"best_accuracy": 0.0367,
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"latest_val_accuracy": 0.0367,
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"latest_f1_score": 0.018325724701464197,
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"total_time_sec_so_far": 1325.2561547756195,
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"total_energy_j_so_far": 99477.21524751316,
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"total_co2_kg_so_far": 0.013125465900713541,
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"peak_vram_mb": 12862.6044921875,
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"amp_enabled": false,
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"scheduler": "cosine",
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"gradient_accumulation_steps": 1,
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"l1_lambda": 0.0,
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"eag_enabled": false,
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"batch_size": 96,
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"image_size": 64,
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"status": "running",
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"updated_utc": "2026-05-15T02:20:48Z"
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}
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