End of training

README.md

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+---
+license: mit
+base_model: google/vivit-b-16x2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: vivit-b-16x2-finetuned-cctv-surveillance
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# vivit-b-16x2-finetuned-cctv-surveillance
+
+This model is a fine-tuned version of [google/vivit-b-16x2](https://huggingface.co/google/vivit-b-16x2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1478
+- Accuracy: 0.9460
+- F1: 0.9430
+- Recall: 0.9460
+- Precision: 0.9454
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-06
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 4176
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.9564        | 0.12  | 522  | 0.4417          | 0.8685   | 0.8096 | 0.8685 | 0.7990    |
+| 0.4574        | 1.12  | 1044 | 0.2633          | 0.9131   | 0.9042 | 0.9131 | 0.9269    |
+| 0.421         | 2.12  | 1566 | 0.1875          | 0.9272   | 0.9100 | 0.9272 | 0.9353    |
+| 0.4785        | 3.12  | 2088 | 0.1854          | 0.9249   | 0.9082 | 0.9249 | 0.9140    |
+| 0.3213        | 4.12  | 2610 | 0.1805          | 0.9272   | 0.9125 | 0.9272 | 0.9216    |
+| 0.1465        | 5.12  | 3132 | 0.1733          | 0.9413   | 0.9362 | 0.9413 | 0.9398    |
+| 0.0784        | 6.12  | 3654 | 0.1616          | 0.9437   | 0.9391 | 0.9437 | 0.9434    |
+| 0.2017        | 7.12  | 4176 | 0.1478          | 0.9460   | 0.9430 | 0.9460 | 0.9454    |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "google/vivit-b-16x2",
+  "architectures": [
+    "VivitForVideoClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "hidden_act": "gelu_fast",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "burglary",
+    "1": "normal",
+    "2": "roadaccidents",
+    "3": "robbery",
+    "4": "stealing"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "burglary": 0,
+    "normal": 1,
+    "roadaccidents": 2,
+    "robbery": 3,
+    "stealing": 4
+  },
+  "layer_norm_eps": 1e-06,
+  "model_type": "vivit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_frames": 32,
+  "num_hidden_layers": 12,
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "tubelet_size": [
+    2,
+    16,
+    16
+  ]
+}

model.safetensors

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+size 354624604

preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "videos",
+    "do_resize",
+    "size",
+    "resample",
+    "do_center_crop",
+    "crop_size",
+    "do_rescale",
+    "rescale_factor",
+    "offset",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_normalize": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "do_zero_centering": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "VivitImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "offset": true,
+  "resample": 2,
+  "rescale_factor": 0.00784313725490196,
+  "size": {
+    "shortest_edge": 256
+  }
+}

runs/May25_09-57-28_a97e9f363930/events.out.tfevents.1716631053.a97e9f363930.34.0

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training_args.bin

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